Paper Info
Title
Dimension reduction using clustering algorithm and rough set theory
Abstract
In real world, datasets have large number of attributes but few are important to describe them properly. The paper proposes a novel dimension reduction algorithm for real valued dataset using the concept of Rough Set Theory and clustering algorithm to generate the reduct. Here, projection of dataset based on two conditional attributes Ci and Cj is taken and K-means Clustering algorithm is applied on it with K = number of distinct values of decision attribute D of the dataset to obtain K clusters. Also the dataset is clustered into K-groups using Indiscernibility relation applied on the decision attribute D. Then the connecting factor k of combined conditional attributes (CiCj) with respect to D is calculated using two cluster sets and attribute connecting set ACS = {(CiCj$\rightarrow^{\hspace*{-2.5mm}^k} D$) for all Ci,Cj ∈ C, Conditional attribute set, and D (Decision attribute)} is formed. Each element (CiCj$\rightarrow^{\hspace*{-2.5mm}^k} D$) ∈ ACS implies that Ci and Cj connecting together partition the objects that yields (k*100) % similar partitions as made on D. Now an undirected weighted graph with weights as the connecting factor k is constructed using attribute connecting set ACS. Finally based on the weight associated with edges, the important attributes, called reduct are generated. Experimental result shows the efficiency of the proposed method.
Year
DOI
Venue
2012
10.1007/978-3-642-35380-2_82
SEMCCO
Keywords
Field
DocType
important attribute,decision attribute,combined conditional attribute,rough set theory,conditional attribute,clustering algorithm,k cluster,novel dimension reduction algorithm,conditional attribute set,factor k,cluster set,dimension reduction
Cluster (physics),Reduct,Dimensionality reduction,Artificial intelligence,Cluster analysis,Attribute domain,Discrete mathematics,Graph,Combinatorics,Rough set,Partition (number theory),Machine learning,Mathematics
Conference
Volume
ISSN
Citations 
7677
0302-9743
1
PageRank 
References 
Authors
0.35
5
2
Name
Order
Citations
PageRank
Shampa Sengupta1141.59
Asit Kumar Das27316.06