원문정보
초록
영어
Clustering is a challenging task in data mining technique. The aim of clustering is to group the similar data into number of clusters. Various clustering algorithms have been developed to group data into clusters. However, these clustering algorithms work effectively either on pure numeric data or on pure categorical data, most of them perform poorly on mixed categorical and numerical data types in previous k-means algorithm was used but it is not accurate for large datasets. In this paper we cluster the mixed numeric and categorical data set in efficient manner. In this paper we present a clustering algorithm based on similarity weight and filter method paradigm that works well for data with mixed numeric and categorical features. We propose a modified description of cluster center to overcome the numeric data only limitation and provide a better characterization of clusters. The performance of this algorithm has been studied on benchmark data sets.
목차
1. Introduction
2. Related Work
2.1 Cluster Ensemble Approach for Mixed Data
2.2 Methodology
3. Review of K-means Algorithm
3.1 K-Means
3.2 K-Prototype
4. Proposed Algorithm
4.1 Similarity Weight Method
4.2 Clustering Similarity Analysis
4.3. Filter Algorithm
4.3 Advantages of Proposed System
5. Clustering results
6. Conclusion
References