Aggregating Multiple Instances in Relational Database Using Semi-Supervised Genetic Algorithm-based Clustering Technique

Rayner Alfred; Dimitar Kazakov

Aggregating Multiple Instances in Relational Database Using Semi-Supervised Genetic Algorithm-based Clustering Technique

Computer Science

Research output: Contribution to conference › Paper › peer-review

Abstract

In solving the classification problem in relational data mining,
traditional methods, for example, the C4.5 and its variants, usually require data
transformations from datasets stored in multiple tables into a single table.
Unfortunately, we may loss some information when we join tables with a high
degree of one-to-many association. Therefore, data transformation becomes a
tedious trial-and-error work and the classification result is often not very
promising especially when the number of tables and the degree of one-to-many
association are large. In this paper, we propose a genetic semi-supervised
clustering technique as a means of aggregating data in multiple tables for the
classification problem in relational database. This algorithm is suitable for
classification of datasets with a high degree of one-to-many associations. It can
be used in two ways. One is user-controlled clustering, where the user may
control the result of clustering by varying the compactness of the spherical
cluster. The other is automatic clustering, where a non-overlap clustering
strategy is applied. In this paper, we use the latter method to dynamically
cluster multiple instances, as a means of aggregating them, and illustrate the
effectiveness of this method using the semi-supervised genetic algorithm-based
clustering technique.

Original language	Undefined/Unknown
Publication status	Published - 2007

Access to Document

http://ceur-ws.org/Vol-325/paper13.pdf

Cite this

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title = "Aggregating Multiple Instances in Relational Database Using Semi-Supervised Genetic Algorithm-based Clustering Technique",

abstract = "In solving the classification problem in relational data mining,traditional methods, for example, the C4.5 and its variants, usually require datatransformations from datasets stored in multiple tables into a single table.Unfortunately, we may loss some information when we join tables with a highdegree of one-to-many association. Therefore, data transformation becomes atedious trial-and-error work and the classification result is often not verypromising especially when the number of tables and the degree of one-to-manyassociation are large. In this paper, we propose a genetic semi-supervisedclustering technique as a means of aggregating data in multiple tables for theclassification problem in relational database. This algorithm is suitable forclassification of datasets with a high degree of one-to-many associations. It canbe used in two ways. One is user-controlled clustering, where the user maycontrol the result of clustering by varying the compactness of the sphericalcluster. The other is automatic clustering, where a non-overlap clusteringstrategy is applied. In this paper, we use the latter method to dynamicallycluster multiple instances, as a means of aggregating them, and illustrate theeffectiveness of this method using the semi-supervised genetic algorithm-basedclustering technique.",

author = "Rayner Alfred and Dimitar Kazakov",

year = "2007",

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TY - CONF

T1 - Aggregating Multiple Instances in Relational Database Using Semi-Supervised Genetic Algorithm-based Clustering Technique

AU - Alfred, Rayner

AU - Kazakov, Dimitar

PY - 2007

Y1 - 2007

N2 - In solving the classification problem in relational data mining,traditional methods, for example, the C4.5 and its variants, usually require datatransformations from datasets stored in multiple tables into a single table.Unfortunately, we may loss some information when we join tables with a highdegree of one-to-many association. Therefore, data transformation becomes atedious trial-and-error work and the classification result is often not verypromising especially when the number of tables and the degree of one-to-manyassociation are large. In this paper, we propose a genetic semi-supervisedclustering technique as a means of aggregating data in multiple tables for theclassification problem in relational database. This algorithm is suitable forclassification of datasets with a high degree of one-to-many associations. It canbe used in two ways. One is user-controlled clustering, where the user maycontrol the result of clustering by varying the compactness of the sphericalcluster. The other is automatic clustering, where a non-overlap clusteringstrategy is applied. In this paper, we use the latter method to dynamicallycluster multiple instances, as a means of aggregating them, and illustrate theeffectiveness of this method using the semi-supervised genetic algorithm-basedclustering technique.

AB - In solving the classification problem in relational data mining,traditional methods, for example, the C4.5 and its variants, usually require datatransformations from datasets stored in multiple tables into a single table.Unfortunately, we may loss some information when we join tables with a highdegree of one-to-many association. Therefore, data transformation becomes atedious trial-and-error work and the classification result is often not verypromising especially when the number of tables and the degree of one-to-manyassociation are large. In this paper, we propose a genetic semi-supervisedclustering technique as a means of aggregating data in multiple tables for theclassification problem in relational database. This algorithm is suitable forclassification of datasets with a high degree of one-to-many associations. It canbe used in two ways. One is user-controlled clustering, where the user maycontrol the result of clustering by varying the compactness of the sphericalcluster. The other is automatic clustering, where a non-overlap clusteringstrategy is applied. In this paper, we use the latter method to dynamicallycluster multiple instances, as a means of aggregating them, and illustrate theeffectiveness of this method using the semi-supervised genetic algorithm-basedclustering technique.

M3 - Paper

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