Enhanced Cluster Ensemble Approach Using Multiple Attributes in Unreliable Categorical Data
1Deena Babu Mandru and Y.K. Sundara Krishna
Cluster analysis is efficient tool to identify useful and user preferable data patterns from Categorical data streams. Conventional clustering approaches focused on numerical with single attribute relations from categorical data. Existing approaches performs poor and low complexity to combine relative attributes whether information is present or hidden. Therefore, our proposed Enhanced Categorical Cluster Ensemble Approach (ECCEA) to classify data depends on various different attributes from multi dimensional data sources. ECCEA creates a matrix and then converts this matrix into attribute groups with help of graph method. Practical outcomes shows an effective clustering result with multi attribute relations with respect to associated attributes from categorical data sets. Further improvement of our proposed approach is to perform well on their corresponding type of attributes to improve the performance with respect to multi-attribute similarity determine for feature-based data exploration using clustering.
K-Means, Uncertain One Class Classifier, Cluster Ensemble Approach, Support Vector mechanism, Feature Representation.