Sanstech

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Archive for April, 2012

My Master’s Dissertation, Revisited Series – Part 2:

Posted by sanstechbytes on April 30, 2012

In part 1, we had a quick overview of data mining and text (data) mining and some terminology used. Let’s explore clustering in this part, some of the clustering algorithms and text clustering using text mining.

In clustering, the objective is to partition unstructured set of objects to clusters or groups. We often want that the objects to be as similar as possible to objects in the same cluster and as dissimilar to objects from other clusters as possible.

Clustering vs. Categorization

By automatic categorization, we mean to let a machine decide to which of a set of predefined categories a text belongs. In clustering, the machine decides how a given text set should be partitioned. Categorization is suitable when one wants to categorize new texts according to a known categorization, clustering when one wants to discover new structures not previously known. Both methods may give interesting results on an unknown text set; categorization sorts them according to a well-known structure, clustering displays the structure of the particular set. This thesis deals with clustering of texts.

Let’s look at an example for visualization of Clusters for Customer groups based on their age and income, for simplicity. I think the diagrams (Courtesy: Java Data Mining : Strategy, Standard and Practice book by Hornick, Venkayala, Marcade) are pretty much self-explanatory.

Customer Clusters for above set of data:

Cluster Model – Histograms

 

Clustering is an unsupervised learning method. The result (the clustering, the partition) is based solely on the  object representation, the similarity measure and the clustering algorithm. If these correspond to the users understanding the result might well be an intuitive and useful clustering. One must keep in mind, though, that clustering algorithms always produce clusterings, even when this is not justified, and that there in most cases exist many relevant clusterings of a set of complex objects.

There are several different approaches to the computation of clusters. Clustering algorithms may be characterized as:

  • Hierarchical — Groups data objects into a hierarchy of clusters. The hierarchy can be formed top-down or bottom-up. Hierarchical methods rely on a distance function to measure the similarity between clusters.

  • Partitioning — Partitions data objects into a given number of clusters. The clusters are formed in order to optimize an objective criterion such as distance.

  • Locality-based — Groups neighboring data objects into clusters based on local conditions.

  • Grid-based — Divides the input space into hyper-rectangular cells, discards the low-density cells, and then combines adjacent high-density cells to form clusters.

Let’s look at some algorithms. Let’s explore basic and popular k-Means algorithm for clustering.

k-Means Algorithm:

The k-Means algorithm is a distance-based clustering algorithm that partitions the data into a predetermined number of clusters provided there are enough distinct cases. In the basic version of the k-means, we first choose K initial centroids, where K is a cluster specified parameter, viz., the number of clusters desired. Each point is then assigned to the closest centroid, and each collections of points assigned to a centroid is a cluster. The centroid of each cluster is then updated based on the points assigned to the the cluster. We repeat the assignment and update steps until no point changes clusters, or equivalently, until the centroids remain the same.

Distance-based algorithms rely on distance metric (function) to measure the similarity between data points. The distance metric is either Euclidean, Cosine, or Fast Cosine distance. Data points are assigned to the nearest cluster according to the distance metric used.

Basic k-means is formally described as below:

1. Select K points as initial Centroids

2. repeat:

3.    Form K clusters by assigining each point to it’s closest centroid..

4.    Recompute the centroid of each cluster.

5. until: Centroids do not change

The operation of above k-means algorithm is illustrated in below figure, which shows, how starting from three centroids, the final clusters are found in four assignment-update steps. In these and other figures displaying k-means clustering, each subfigure shows (1) the centroids at the start of the iteration and (2) the assignment of the points to those centroids. The centroids are indicated by the “+” symbol; all points belonging to the same cluster have the same marker shape.

(Courtesy: Introduction to Data Mining: Tan, Vipin Kumar, Steinbach)

Now, let’s look at enhanced k-Means algorithm supported by ODM for clustering.

Enhanced k-means –

  • Builds models in a hierarchical manner. The algorithm builds a model top down using binary splits and refinement of all nodes at the end. In this sense, the algorithm is similar to the bisecting k-means algorithm. The centroid of the inner nodes in the hierarchy is updated to reflect changes as the tree evolves. The whole tree is returned.

  • Grows the tree one node at a time (unbalanced approach). Based on a user setting available in either of the programming interfaces, the node with the largest variance is split to increase the size of the tree until the desired number of clusters is reached. The maximum number of clusters is specified in the build setting for clustering models, CLUS_NUM_CLUSTERS.

  • Provides probabilistic scoring and assignment of data to clusters.

  • Returns, for each cluster, a centroid (cluster prototype), histograms (one for each attribute), and a  rule describing the hyperbox that encloses the majority of the data assigned to the cluster. The centroid reports the mode for categorical attributes or the mean and variance for numerical attributes.

This approach to k-means avoids the need for building multiple k-means models and provides clustering results that are consistently superior to the traditional k-means.

I used Oracle Data Mining (ODM) API’s that use enhanced k-means algorithm described above, for k-means clustering. O-Cluster algorithm implementation is also provided in ODM for clustering.

Time and Space Complexity:

Since only the centroids and data points are stored, the storage required is O((m + K)n), where m is the number of points and n is the number of attributes. The time required (time complexity) is modest too, and can be determined to be: O(I * K * m * n), where I is the number of iterations required for convergence.

‘I’ is often small and can usually be safely bounded, as most changes typically occur in the first few iterations. Hence, K-means is linear in m, the number of points, provided K, no of clusters, is significantly less than m.

Text Clustering 

Text Clustering is a specific task in text mining that refers to the process of deriving high-quality information from text and is used for unsupervised document organization, automatic topic extraction and fast information retrieval or filtering.

The main applications of clustering in text mining are:
■  Simple clustering. This refers to the creation of clusters of text features .  For example: grouping the hits returned by a search engine.

■  Taxonomy generation. This refers to the generation of hierarchical groupings. For example: a cluster that includes text about car manufacturers is the parent of child clusters that include text about car models.

■  Topic extraction. This refers to the extraction of the most typical features of a group. For example: the most typical characteristics of documents in each document topic.

Let’s talk about Simple Clustering a bit. Suppose you’ve a call center web application and your call center reps enter the comments about a particular offering (product) or in general about the call center experience on your site, and those comments are recorded in your database tables. To make sense of this unstructured data,  i.e. to know which group your customers belong to – HAPPY, SATISFIED, UNHAPPY etc or customer’s level of acceptance about a particular product, and come up with some marketing strategy to improve  customer call center experience or product usability, you want to mine the comments (text) and apply k-means algorithm to group comments. Remember, we’re talking about hundreds of thousands of customers and comments and add to that unstructured data (Comments can contain alphabets, numbers, special chars, image etc). Any traditional smart code to retrieve data and group them, is not apt here and is out of contention, for such a scale.

To apply clustering algorithm for above scenario, say k-means, you need to have the text undergo a special pre-processing step known as Term Extraction or Feature Extraction. This process breaks the text down into units (terms) that can be mined. Text terms may be keywords or other document-derived features . The parameters like Semantic similarity, similar words stop words  and weighting schemes like TF-iDF may be considered while applying a text extraction algorithm like NMF (refer to part 1). You may want to refer to Text Mining section in part 1 of this series, for some theory.

I used ODM API’s. Oracle enhanced k-means algorithm (see below for this) supports text mining. Oracle Data Miner graphical tool performs term extraction transparently when you create or apply a text mining model. The Oracle Data Mining Java API provides term extraction classes for use in Java applications. If you are using the PL/SQL API, you can use a set of Oracle Text table functions to extract the terms. For screenshots of clustering model and Term Extraction model, please watch out for part 3 in this series.

Suppose you want to predict if customers will increase spending with an affinity card. You want to include the comments field from a customer survey as one of the predictors. Before building the classification model, you want to create a reduced set of text features to improve the predictive power of the comments. To do this, you will create a feature extraction model.

In the next part, we’ll look at ODM API’s and my dissertation repository for code, results, links and pdfs.

Posted in Data Mining, Research | Leave a Comment »

My Master’s Dissertation, Revisited Series – Part 1:

Posted by sanstechbytes on April 28, 2012

As I had mentioned in one of my previous posts that I would talk about my Master’s dissertation in my future posts, I’m happy that now is the time. The dissertation was titled “Text Clustering in a Call Centre Application using Data Mining”. My primary motivation was to explore data mining concepts and in particular clustering and see how it can connect to real-world problems in CRM domain. Being a java developer, I found it easier to quickly use Oracle Data Mining (ODM) API built on top of Java Data Mining (JSR Spec – http://www.jcp.org/en/jsr/detail?id=247) for my proof-of-concept.

In this three-part series of “My Master’s Dissertation, Revisited”, I’ll give a quick overview of data mining, text mining(in particular) and some terminology used in this series, in the first part. In part two of this series, I’ll talk about Text Clustering, enhanced k-means algorithm for clustering, and the sample that I used. I’ll wrap up this series by explaining as to how I applied k-means model, the code that I customized, and the results with a link to the sample code zip file.

I’ll be focusing on data mining concepts and functions relevant to my dissertation in this series. For info on other mining functions or more details, please refer to my previous post on the topic.

What is Data Mining? Why is it used?

Data mining is the practice of automatically searching large stores of data to discover patterns and trends that go beyond simple analysis and simple querying.  Data Mining is, in plain terms, making sense of the data. The key properties of data mining are:

i) Automatic discovery of patterns

Data mining is accomplished by building models. A model uses an algorithm to act on a set of data. The notion of automatic discovery refers to the execution of the data mining models.

ii) Prediction of likely outcomes:

A model might predict income based on education and other demographic factors. A data mining technique can generate a rule might specify that a person who has a bachelor’s degree and lives in a certain neighborhood is likely to have an income greater than the regional average.

iii) Creation of actionable information:

A town planner might use a model that predicts income based on demographics to develop a plan for low-income housing. A car leasing agency might a use model that identifies customer segments to design a promotion targeting high-value customers.

iv) Focus on large data sets and databases:

When you want to retrieve data from thousands or millions of records that have tens or hundreds of columns (attributes or dimensions) like transactional data (eg: customer data) from table, data mining models make lot of sense.

What it can do and what it cannot?

Data mining discovers hidden information in your data, but it cannot tell you the value of the information to your organization.  You might already be aware of important patterns as a result of working with your data over time. Data mining can confirm or qualify such empirical observations in addition to finding new patterns that may not be immediately discernible through simple observation.

Steps in Data Mining Process:

The steps in the process of data mining can diagrammatically be represented as:

1. Problem Definition:

This initial phase of a data mining project focuses on understanding the project objectives and requirements.

For example, your business problem might be: “How can I sell more of my product to customers?” You might translate this into a data mining problem such as: “Which customers are most likely to purchase the product?” A model that predicts who is most likely to purchase the product must be built on data that describes the customers who have purchased the product in the past.

2. Data Gathering and Preparation:

The data understanding phase involves data collection and exploration. As you take a closer look at the data, you can determine how well it addresses the business problem. You might decide to remove some of the data or add additional data. This is also the time to identify data quality problems and to scan for patterns in the data. For example, you might transform a DATE_OF_BIRTH column to AGE; you might insert the average income in cases where the INCOME column is null.

3. Model Building and Evaluation:

In this phase, you select and apply various modeling techniques and calibrate the parameters to optimal values. If the algorithm requires data transformations, you will need to step back to the previous phase to implement them.

In preliminary model building, it often makes sense to work with a reduced set of data (fewer rows in the case table), since the final case table might contain thousands or millions of cases. Based on the extent to which the model has to be improved, we can increase the number of attributes.

4. Knowledge Deployment:

Knowledge deployment is the use of data mining within a target environment. In the deployment phase, insight and actionable information can be derived from data.

Deployment can involve scoring (the application of models to new data), the extraction of model details (for example the rules of a decision tree), or the integration of data mining models within applications, data warehouse infrastructure, or query and reporting tools. For example, a sales representative could run a model that predicts the likelihood of fraud within the context of an online sales transaction.

Data Mining Functions

Data mining functions can be broadly classified into Supervised and Unsupervised mining functions. Each data mining function uses one or more algorithms to build a model.

The building of a supervised model involves training, a process whereby the software analyzes many cases where the target value is already known. In the training process, the model “learns” the logic for making the prediction. For example, a model that seeks to identify the customers who are likely to respond to a promotion must be trained by mining functions analyzing the characteristics of many customers who are known to have responded or not responded to a promotion in the past.

The building of an unsupervised model doesn’t involve training, as there is no previously-known result to guide the algorithm in building the model. For example, clustering models use descriptive data mining techniques, but they can be applied to classify cases according to their cluster assignments. Anomaly detection, although unsupervised, is typically used to predict whether a data point is typical among a set of cases.

Let’s have a quick look at how can these functions help a data miner achieve and the business context in which they’re used.

Association:

The goal of the Associations mining function is to find items that are consistently associated with each other in a meaningful way. For example, you can analyze purchase transactions to discover combinations of goods that are often purchased together. The Associations mining function answers the question: If certain items are present in a transaction, what other item or items are likely to be present in the same transaction?

Classification:

The process of automatically creating a model of classes from a set of records that contain class labels. The classification technique analyzes records that are already known to belong to a certain class, and creates a profile for a member of that class from the common characteristics of the records. You can then use a data mining scoring tool to apply this Classification model to new records, that is, records that have not yet been classified. This enables you to predict if the new records belong to that particular class.

Commercial applications of this mining function include credit card scoring, ranking of customers for directed mailing, the prediction of credit risk in new customers, and attrition prediction.

Regression:

Regression is similar to classification except for the type of the predicted value. Classification predicts a class label, regression predicts a numeric value. Regression also can determine the input fields that are most relevant to predict the target field values. The predicted value might not be identical to any value contained in the data that is used to build the model. An example application is customer ranking by expected profit.

Clustering:

The Clustering mining function includes algorithms like k-means, O-cluster etc. It groups similar customers together. At the same time it maximizes the differences between the different customer groups that are formed in this way.

The groups that are found are known as clusters. Each cluster tells a specific story about customer identity or behavior, for example, about their demographic background, or about their preferred products or product combinations. In this way, customers that are similar are grouped together in homogeneous groups that are then available for marketing or for other business processes.

In the commercial environment, clustering is used in the areas like: Cross-marketing, Cross-selling, Customizing marketing plans for different customer types, Deciding which media approach to use, Understanding shopping goals, and Many other areas.

Clustering can also be used for anomaly detection. Once the data has been segmented into clusters, you might find that some cases do not fit well into any clusters. These cases are anomalies or outliers.

The prerequisites to understand text clustering and in particular k-means algorithm as well as some other mining functions are: Understanding of Numerical, Categorical Attributes, Data Transformation techniques, Euclidean Distance, Document Matrix, Vector Space Model (VSM), Text Mining. See section ‘Some Terminology’ below.

Some Terminology:

Numerical Attribute: An attribute whose values are numbers. The numeric value can be either an integer or a real number.

Categorical Attribute: An attribute where the values correspond to discrete categories. For example, state is a categorical attribute with discrete values (CA, NY, MA, etc.). Categorical attributes are either non-ordered (nominal) like state, gender, and so on, or ordered (ordinal) such as high, medium, or low temperatures.

Binning:  This transformation maps a set of input values to a smaller set of bins. The input values may be discrete or continuous.

Normalization: It maps numerical values to a particular numerical range, typically [0…1]. There are several types of normalization (e.g., z-score, min-max, and shift-scale).

Explosion: This transformation applies only to discrete data such as strings or numbers representing individual categories. The goal is to transform such attributes into numerical attributes.

Support: The ratio of the number of occurrences of R, given all occurrences of all rules.

Confidence: The confidence of a rule X->Y, is the ratio of the number of occurrences of Y given X, among all other occurrences given X.

Euclidean Distance: The Euclidean distance or Euclidean metric is the “ordinary” distance between two points that one would measure with a ruler, and is given by the Pythagorean formula. In Cartesian coordinates, if p = (p1, p2,…, pn) and q = (q1, q2,…, qn) are two points in Euclidean n-space, then the distance from p to q, or from q to p is given by:

d(p, q) = d(q, p) = √ (q1 – p1)2 + (q2 – p2)2  + ….. + (qn – pn)2  =  √∑( qi – pi)2

For N dimensions

In general, for an n-dimensional space, the distance is:

d(p, q) = √ (p1 – q1)2 + (p2 – q2)2  + … + (pi – qi)2  + … + (pn – qn)2  =  √∑( qi – pi)2

Vector Space Model: In the vector space model each text in a set of texts is represented by a vector in a high-dimensional space, with as many dimensions as the number of different words in the set. Each text gets weights (values) in the indices (dimensions) based on what words appear in them. These weights model how important the corresponding word is deemed to be to explain the content of the text. They are dependent on whether (and how often) the word appears in the document and in the entire set. Texts whose vectors are close to each other in this space are considered being similar in content.

Text Mining

Text mining, sometimes alternately referred to as text data mining, roughly equivalent to text analytics, refers to the process of deriving high-quality information from text. ‘High quality’ in text mining usually refers to some combination of relevance, novelty, and interestingness. Typical text mining tasks include text categorization, text clustering, concept/entity extraction, production of granular taxonomies, sentiment  analysis, document summarization, and entity relation modeling (i.e., learning relations between named entities).

Unstructured data like ppt, audio, images, call center notes etc can be converted into numerical attribute using Text Mining. The term extraction process might involve application of NMF (Non-Negative Matrix Factorization) algorithms and treats the text in each row of the original table as a separate document. Each document is transformed to a set of terms that have a numeric value and a text label.

Most of the text-mining methods are grounded in the term-based VSM to calculate the distance or similarity between documents. Let’s look at some definitions for using document representation of vector.

A document is commonly represented as a vector of terms in a VSM. The basis of the vector collection. In a simple way, we can use a word to be a term. Yet, morphological variants like ’actor’, ’action’ and ’acting’ are so closely related that they are usually conflated into a single word stem, e.g., ’act’ by stemming [4], [5]. After stemming, two word stems are treated as unrelated if they are different. For example, the stem of ’suggestion’ and ’advice’ are usually considered unrelated despite of their apparent relationship. Since different word stems are considered unrelated, i.e., independent, the base vectors in VSM are orthogonal to each other.

A set of documents X in traditional term-based VSM is represented by X = {X1, X2…… Xn}. One document is represented by Xj = {xj1, xj2,…..xj,m}. Terms in the vocabulary extracted from the corresponding document collection are represented by {t1, t2…tm}.

Where xjii is the weight of the term tii in document Xj and is usually determined by the number of times ti appears in Xj (known as the term frequency). Other weighting scheme like TF-iDF can also be applied.

Based on the definition of VSM, distance between two text documents X1 and X2 can be easily computed. There are many methods to measure this distance, such as: Cosine similarity and Minkowski distance including Euclidean distance, Manhattan distance and Maximum distance. Here, we give the definition of Euclidean distance which is effective and frequently used in text clustering.

Euclidean distance of two documents X1 and X2  in the VSM, is defined as:

D(X1, X2) = √( X1 – X2 ) ( X1 – X2 )   T

     = √i=1∑m( x1i-x2i)2

The smaller the value of d(X1, X2) is, the more similar the two documents are. From above equation, we can see that this distance definition does not take into account any patterns of term correlation that exist in  the real-world data.

Posted in Data Mining, Research | 1 Comment »

Number to Text Representation Problem

Posted by sanstechbytes on April 22, 2012

Problem:

Given a number (up to a billion), display the string (text) representation of it. For eg.: 123456789 should be represented as “One Hundred and Twenty Three Million Four Hundred and Fifty Six thousand Seven Hundred and Eighty Nine. ‘and’ should only be used after hundred.

Solution:

Approach 1: In a OO approach, a number can be represented by Number class. Each Number object has a list of Triplet objects (composition). Triplet represents a group of max 3 digits and can’t exist without a Number.  With this approach, it’s easy to accommodate different text representation systems like British English International Unit (million etc), British English Indian Units (lac, crore).

I wrote the classes (see below) for both approaches including JUnit Tests. I’ve captured the outputs for sample runs in the project folder that you can download here.

Approach 2: Assume Text representation in British English. Think of converting 123456789 into “One Hundred and Twenty Three Million Four Hundred and Fifty Six thousand Seven Hundred and Eighty Nine.

So, convert(123456789) = convert(123) + “million” + convert(456) + “thousand” + convert(789). [Courtesy: Gayle Laakmann of careercup.com]. This makes use of the technique of recursion. A lot of new code has to be written to extend this behavior though. There’re too many special cases to handle.

Code: Approach 1:


package numbertextoop;

import java.util.LinkedList;
import java.util.List;

import utils.NumberTextUtils;

/**
* This class represents a given number. Can be used to display in British
* English phrase the number Eg: 1234 in British English phrase: One Thousand
* Two Hundred and Thirty Four This class can accommodate text representation
* types like British, Indian etc with the constructor provided 04/14/2012 @author
* sanjeev 04/19/2012 @author sanjeev
*
* @version 1.1
*/

public class Number {

private int num;
private List<Triplet> triplets;

/**
* Assume default text representation type as British
*/
public Number(int i) {

this.num = i;

/**
* Handle negative number
*/
if (i < 0) {
i = -1 * i;
}

int length = Integer.toString(i).length();

if (length % NumberTextUtils.THREE == 0) {
length = length / NumberTextUtils.THREE;
} else if (length % NumberTextUtils.THREE > 0) {
length = length / NumberTextUtils.THREE + 1;
}

this.triplets = new LinkedList<Triplet>();
buildTriplets(i, length);
}

/**
* Allows for specifying text representation system, British, Indian etc
* text representationType could be enum - TextRepresentationType.BRITISH,
* TextRepresentationType.INDIAN. In Case of 'Indian', constants like Lac,
* Crore, should be added appropriately in NumberTextUtils.java
*/
public Number(int i, String representationType) {

}

/**
* @param num
* @param length
*            Builds triplet objects and arranges the position (hundredth,
*            tenth, unit) of the digits forming a triplet
*/
private void buildTriplets(int num, int length) {
while (length != 0) {
int tripletValue = num % 1000;

Triplet triplet = new Triplet(tripletValue);
triplet = triplet.matchTripletPlaces(tripletValue);

System.out.println("triplet" + triplet.getHundredthPlace() + "."
+ triplet.getTenthPlace() + ":" + triplet.getUnitPlace());
((LinkedList<Triplet>) this.triplets).addFirst(triplet);

num = num / NumberTextUtils.THOUSAND;
length--;
}
}

/**
* String representation of this number
*
*/
public String toString() {

/**
* When num = 0, returns "Zero" or if accessed thro
*
*/
if (this.num == 0) {
return NumberTextUtils.ZERO_TEXT;
}

StringBuilder numTextBuilder = new StringBuilder();

/**
* Negative number
*
*/
if (this.num < 0) {
numTextBuilder.append("Negative ");
}

return constructEnglishText(this.triplets, numTextBuilder).toString();
}

/**
* Constructs the British English text from this group of triplets formed
* from
*
* @param num
* @param triplets
*            List
*/
private StringBuilder constructEnglishText(List<Triplet> triplets,
StringBuilder numTextBuilder) {
int mapIndex = triplets.size();

for (Triplet triplet : triplets) {
numTextBuilder = numTextBuilder.append(triplet.toString()).append(
" ");

/**
* When num = 1,000,000: print only one million
*/
if (triplet.getHundredthPlace() == 0
&& triplet.getTenthPlace() == 0
&& triplet.getUnitPlace() == 0
&& mapIndex < this.triplets.size()) {
numTextBuilder = numTextBuilder.append("");
} else {
numTextBuilder = numTextBuilder.append(
NumberTextUtils.tripletMap().get(mapIndex)).append(" ");
}

mapIndex--;
}

return numTextBuilder;
}

public int getNum() {
return num;
}

public String getText() {
return this.toString();
}

public List<Triplet> getTriplets() {
return triplets;
}

}

 

package numbertextoop;

import utils.NumberTextUtils;

/**
* This class represents a group of digits up to a maximum of 3 that form a
* given number. 04/14/12 @author sanjeev 04/19/12 @author sanjeev
*
* @version 1.1
*/
public class Triplet {

private int hundredthPlace;
private int tenthPlace;
private int unitPlace;
private int value;

public Triplet(int tripletGroupNum) {
this.value = tripletGroupNum;
}

/**
* Arranges the place of digits for hundredth, tenth and unit positions in
* this triplet
*
* @param tripletValue
* @return Triplet
*/
public Triplet matchTripletPlaces(int tripletValue) {
this.setHundredthPlace(tripletValue / NumberTextUtils.HUNDRED);

tripletValue = tripletValue % NumberTextUtils.HUNDRED;
this.setTenthPlace(tripletValue / NumberTextUtils.TEN);

tripletValue = tripletValue % NumberTextUtils.TEN;
this.setUnitPlace(tripletValue);

return this;
}

/**
* Returns the string representation of this triplet
*
* @see java.lang.Object#toString()
*/
public String toString() {
return constructTripletText().toString();
}

/**
* @return tripletText StringBuilder
*/
private StringBuilder constructTripletText() {

StringBuilder tripletText = new StringBuilder();

tripletText = appendHundredthPlaceText(tripletText);

/** 'Teen' candidate check: 13 -> Thirteen, 11 -> Eleven */
StringBuilder teenCandidateBuilder = new StringBuilder();
String teenCandidate = teenCandidateBuilder.append(
String.valueOf(this.tenthPlace)).append(
String.valueOf(this.unitPlace)).toString();
int teenCandidateValue = Integer.parseInt(teenCandidate);

tripletText = appendTenthPlaceText(tripletText, teenCandidateValue);

tripletText = appendUnitPlaceText(tripletText, teenCandidateValue);

return tripletText;
}

/**
* Unit place text candidates Unit place : Five, Zero, Two etc
*/
private StringBuilder appendUnitPlaceText(StringBuilder tripletText,
int teenCandidate) {
/** Print unit place text: 1 -> one, 9 -> nine etc */
if (this.unitPlace != 0) {
if (isATeenNumber(teenCandidate)) {
return tripletText;
}

tripletText = appendDigitText(tripletText, this.unitPlace);
}

return tripletText;
}

/**
* Tenth place and 'Teen' text candidates Tenth place : Five, Eleven,
* Thirteen etc.
*/
private StringBuilder appendTenthPlaceText(StringBuilder tripletText,
int teenCandidate) {
if (this.tenthPlace != 0) {
if (isATeenNumber(teenCandidate)) {
tripletText = tripletText
.append(NumberTextUtils.teens[this.unitPlace]);
} else {
tripletText = tripletText
.append(NumberTextUtils.tens[this.tenthPlace - 1]);
}
tripletText.append(" ");
}

return tripletText;
}

/**
* Hundredth place text candidates Hundredth place : Two Hundred, Two
* Hundred and etc.
*/
private StringBuilder appendHundredthPlaceText(StringBuilder tripletText) {
if (this.hundredthPlace != 0) {
if (this.tenthPlace == 0 && this.unitPlace == 0) {
tripletText = appendDigitText(tripletText, this.hundredthPlace)
.append(" Hundred ");
} else {
tripletText = appendDigitText(tripletText, this.hundredthPlace)
.append(" Hundred and ");
}
}

return tripletText;
}

/**
* @param tripletText
* @param digit
* @return
*/
public StringBuilder appendDigitText(StringBuilder tripletText, int digit) {
tripletText.append(NumberTextUtils.digits[digit]);
return tripletText;
}

/**
* @param teenCandidate
* @return true if the number belongs to [11, 12, 13, 14, 15, 16, 17, 18,
*         19]
*/
public boolean isATeenNumber(int teenCandidate) {
return (teenCandidate >= NumberTextUtils.ELEVEN && teenCandidate < NumberTextUtils.TWENTY);
}

public int getValue() {
return value;
}

public int getHundredthPlace() {
return hundredthPlace;
}

public void setHundredthPlace(int hundredthPlace) {
this.hundredthPlace = hundredthPlace;
}

public int getTenthPlace() {
return tenthPlace;
}

public void setTenthPlace(int tenthPlace) {
this.tenthPlace = tenthPlace;
}

public int getUnitPlace() {
return unitPlace;
}

public void setUnitPlace(int unitPlace) {
this.unitPlace = unitPlace;
}

}

Code: Approach 2:

package numbertextalgo;

import utils.NumberTextUtils;

/**
* This class represents a given number in British English phrase Eg: 1234 in
* British English phrase: One Thousand Two Hundred and Thirty Four
*
* @author sanjeev, 14-Apr-2012
* @version 1.0
*/

public class NumberText {

public NumberText() {

}

public static String numberToString(int number) {
if (number == 0) {
return NumberTextUtils.ZERO_TEXT;
} else if (number < 0) {
return NumberTextUtils.NEGATIVE_TEXT + numberToString(-1 * number);
}

int count = 0;
String str = "";

while (number > 0) {
if (number % NumberTextUtils.THOUSAND != 0) {
str = numberToString100(number % NumberTextUtils.THOUSAND)
+ NumberTextUtils.bigs[count] + " " + str;
number /= NumberTextUtils.THOUSAND;
count++;
}
}

return str;
}

private static String numberToString100(int number) {
String str = "";

/** Convert hundredth place */
if (number >= NumberTextUtils.HUNDRED) {
str += NumberTextUtils.digits[number / NumberTextUtils.HUNDRED - 1]
+ " Hundred and ";
number %= NumberTextUtils.HUNDRED;
}

/** Convert NumberTextUtils.tens place */
if (number >= NumberTextUtils.ELEVEN
&& number <= NumberTextUtils.NINETEEN) {
return str + NumberTextUtils.teens[number - NumberTextUtils.ELEVEN]
+ " ";
} else if (number == NumberTextUtils.TEN
|| number >= NumberTextUtils.TWENTY) {
str += NumberTextUtils.tens[number / NumberTextUtils.TEN - 1] + " ";
number %= NumberTextUtils.TEN;
}

/** Convert ones place */
if (number >= NumberTextUtils.ONE && number <= NumberTextUtils.NINE) {
str += NumberTextUtils.digits[number - 1] + " ";
}

return str;
}
}

 

package utils;

import java.util.HashMap;
import java.util.Map;

/**
* This class is used as a utility class for representing a number in text
* 04/14/12 @author sanjeev 04/19/12 @author sanjeev
*
* @version 1.1
*/
public class NumberTextUtils {

public static String[] digits = new String[] { "Zero", "One", "Two",
"Three", "Four", "Five", "Six", "Seven", "Eight", "Nine" };
public static String[] teens = new String[] { "", "Eleven", "Twelve",
"Thirteen", "Fourteen", "Fifteen", "Sixteen", "Seventeen",
"Eighteen", "Nineteen" };
public static String[] tens = new String[] { "Ten", "Twenty", "Thirty",
"Forty", "Fifty", "Sixty", "Seventy", "Eighty", "Ninety" };
public static String[] bigs = new String[] { "", "Thousand", "Million",
"Billion" };

public static final String NEGATIVE_TEXT = "Negative";
public static final String ZERO_TEXT = "Zero";

public static final int ONE = 1;
public static final int THREE = 3;
public static final int NINE = 9;
public static final int TEN = 10;
public static final int TWENTY = 20;
public static final int ELEVEN = 11;
public static final int NINETEEN = 19;
public static final int HUNDRED = 100;
public static final int THOUSAND = 1000;

public static Map<Integer, String> tripletGroupMap = new HashMap<Integer, String>();

static {
tripletGroupMap.put(1, "");
tripletGroupMap.put(2, "Thousand");
tripletGroupMap.put(3, "Million");
tripletGroupMap.put(4, "Billion");
}

public static Map<Integer, String> tripletMap() {
return tripletGroupMap;
}
} 

 

package numbertextoop;

/**
* Test Class with main() for Number.java
*
* @author sanjeev
*
*/
public class TestNumber {

public static void main(String[] args) {
int num = 1000000000;
Number number = new Number(num);

sop(num + " in British English Phrase using OOP: " + number.getText());
}

private static void sop(Object o) {
System.out.println(o);
}

}

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