Customer Payment Trend Analysis based on Clustering for Predicting the Financial Risk of Business Organizations

With the opening of the Indian economy, many multinational corporations are shifting their manufacturing base to India. This includes setting up green field projects or acquiring established business firms of India. The region of this business unit is expanding globally. The variety and size of the customer base is expanding and the business risk related to bad debts is increasing. Close monitoring and analysis of payment trends helps to predict customer behavior and predict the chances of customer financial strength.
The present manufacturing companies generate and store tremendous amount of data. The amount of data is so huge that manual analysis of the data is difficult. This creates a great demand for data mining to extract useful information buried within these data sets. One of the major concerns that affect companies' investments and profitability is bad debts; this can be reduced by identifying past customer behavior and reaching the suitable payment terms. The Clustering and Prediction module was implemented in WEKA - a free open source software written in Java. This study model can be extended to the development of a general purpose software package to predict payment trends of customers in any organisation.

Autorentext

Prof. Jeeva Jose was awarded PhD in Computer Science from Mahatma Gandhi University, Kerala, India and is a faculty member at BPC College, Kerala. Her passion is teaching and areas of interests include World Wide Web, Data Mining and Cyber laws. She has been in higher education since year 2000 years and has completed three research projects funded by UGC and KSCSTE. She has authored and published five books. She has published more than twenty research papers in various refereed journals and conference proceedings. She has edited three books and has given many invited talks in various conferences. She is a recipient of ACM-W Scholarship provided by Association for Computing Machinery, New York.

Leseprobe
Text Sample:
Chapter 3:
METHODOLOGY:
3.1 Partitioning Method:
There exists a large number of clustering algorithms. The choice of clustering algorithm depends on type of data available and on the particular purpose and application. The method used to identify the clusters in this research is the partitioning method.
Given a database of n objects or data tuples, a partitioning method constructs k-partitions of the data, where each partition represents a cluster and k=n. that is it classifies the data into k groups which together satisfies the following requirements.

1. Each group must contain at least one object.
2. Each object must belong to exactly one group.
   Given k, the number of partitions to construct, a partitioning method creates an initial partitioning. It then uses an iterative relocation technique that attempts to improve the partitioning by moving objects from one group to another. The general criterion of a good partitioning is that objects in the same cluster are close or related to each other whereas objects of different clusters are far apart or very different. There are various kinds of other criteria for judging the quality of partitions.
   To achieve global optimality in partitioning based clusterring would require the exhaustive enumeration of all possible partitions. Most applications adopt one of two popular heuristic methods [1].
3. K-means algorithm, where each cluster is represented by the mean value of the objects in the cluster.
4. K-medoids algorithm, where each cluster is represented by one of the objects located near the center of the cluster. These heuristic clustering methods work well for finding spherical-shaped clusters in small to medium sized databases. To find clusters with complex shapes and for clustering very large data sets, partitioning methods need to be extended.
   In this research, K-means algorithm is used for clustering as it was found most suitable.
   3.2 K-means Algorithm for Partitioning:
   The K-means algorithm is based on centroid technique. The K-means algorithm takes the input parameter k, and partitions a set of n objects into k clusters so that the intra cluster similarity is high but the inter cluster similarity is low. Cluster similarity is measured in regard to the mean value of the objects in a cluster, which can be viewed as the cluster's center of gravity.
   The K-means algorithm proceeds as follows. First it randomly selects k objects, each of which initially represents a cluster mean or center. For each of the remaining objects, an object is assigned to the cluster to which it is most similar based on the distance between the object and the cluster mean. It then computes the new mean for each cluster. This process iterates until the criterion function converges [...].
   3.3 Platform Specification:
   WEKA (Waikato Environment for Knowledge Analysis) is a popular suite of machine learning software written in Java, developed at the University of Waikato. WEKA is free software available under the GNU General Public License [11].
   The GNU General Public License (GNU GPL or simply GPL) is a widely used free software license, originally written by Richard Stallman for the GNU project. It is the license used by the Linux kernel. The GPL is the most popular and well known example of the type of strong copyleft license that requires derived works to be available under the same copyleft. Under this philosophy, the GPL is said to grant the recipients of a computer program the rights of the free software definition and uses copyleft to ensure the freedoms are preserved, even when the work is changed or added to. This is in distinction to permissive free software licenses, of which the BSD licenses are the standard examples.
   The GNU Lesser General Public License (LGPL) is a modified, more permissive, version of the GPL, intended for some software libraries. There is also a GNU Free Documentation License, which was originally intended for use with documentation for GNU softw