Adaptive Fuzzy Clustering for improving classification performance in yeast data set

In data mining, there is inter-category imbalance of data which includes unnecessary data that hinder the formulation of an efficient model. This paper called FSFC+ introduces a new focused sampling based on adaptive fuzzy clustering. By applying FSFC+, the optimal number of clusters was used by ada...

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Bibliographic Details
Published in2008 4th International IEEE Conference Intelligent Systems Vol. 3; pp. 18-2 - 18-7
Main Authors Man Sun Kim, Hyung Jeong Yang, Wooi Ping Cheah
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2008
Subjects
Adaptive Fuzzy clustering
Adaptive systems
cluster validity
Clustering algorithms
Computer science
Data analysis
Data mining
focused sampling
Fungi
Fuzzy sets
Intelligent systems
Sampling methods
selective sampling
Sun
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Summary:In data mining, there is inter-category imbalance of data which includes unnecessary data that hinder the formulation of an efficient model. This paper called FSFC+ introduces a new focused sampling based on adaptive fuzzy clustering. By applying FSFC+, the optimal number of clusters was used by adaptive method. It removes unuseful data that can be obstacles to the formulation of an efficient model. When there is no information about data set, we would evaluate the fitness of partitions produced by cluster validity index. In addition, it is very useful in data analysis because it can quantify the degree of membership of data to multiple clusters.
ISBN:9781424417391
1424417392
ISSN:1541-1672
1941-1294
DOI:10.1109/IS.2008.4670457