NDLI: Handling Datasets in a Multi-Relational Environment: Cluster Dispersion vs Cluster Purity

Content Provider	IEEE Xplore Digital Library
Author	Alfred, R. Kazakov, D.
Copyright Year	2007
Description	Author affiliation: Univ. Malaysia Sabah, Kota Kinabalu (Alfred, R.)
Abstract	Clustering multiple-instances in a multi-relational environment requires data transformations (e.g. data aggregation) from datasets stored in multiple tables into a single table. Unfortunately, most relational databases are limited to a few basic methods of aggregation (e.g. max, min, sum, count, ave) to aggregate continuous and categorical values. Therefore, data transformation is limited only to aggregation of continuous and categorical values. In this paper, to get the best number of clusters, we propose a genetic semi-supervised clustering technique as a means of aggregating data stored in multiple tables. This algorithm is suitable for classification of datasets with a high degree of one-to-many associations, in which a single record has multiple instances that are associated with it. The clustering algorithm can be used in two ways. One is the unsupervised clustering, where the user may control the result of clustering by optimizing the value of cluster dispersion. The other is a semi-supervised clustering, where the user uses an unsupervised clustering method optimized with a genetic algorithm incorporating a measure of classification accuracy used in decision tree algorithm, the GINI index. In this paper, we examine both methods to dynamically cluster multiple instances, as a means of aggregating them, and illustrate the effectiveness of the semi-supervised genetic algorithm-based clustering technique.
Starting Page	196
Ending Page	201
File Size	196856
Page Count	6
File Format	PDF
ISBN	9781424413478
DOI	10.1109/IDAACS.2007.4488404
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-09-06
Publisher Place	Germany
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Relational databases Clustering algorithms Supervised learning Genetic algorithms Decision trees Conferences Data acquisition Aggregates Clustering methods Optimization methods Data Pre-processin Data Aggregation Clustering Semi-supervised Clustering Genetic Algorithm Relational Data Mining
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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