RMSC: Robust modeling of subspace clustering for high dimensional data

2017 ◽  
Author(s):  
K. R. Radhika ◽  
C. N. Pushpa ◽  
J. Thriveni ◽  
K. R. Venugopal
Keyword(s):  
High Dimensional Data ◽  
Subspace Clustering ◽  
High Dimensional ◽  
Robust Modeling

Subspace Clustering of High Dimensional Data Using Differential Evolution

2019 ◽  
pp. 47-74 ◽  
Author(s):  
Parul Agarwal ◽  
Shikha Mehta
Keyword(s):  
Differential Evolution ◽  
Distance Measure ◽  
Dimensional Space ◽  
Clustering Algorithms ◽  
High Dimensional Data ◽  
Subspace Clustering ◽  
High Dimensional ◽  
Dbscan Clustering ◽  
Evolution Algorithms ◽  
Self Adaptive

Subspace clustering approaches cluster high dimensional data in different subspaces. It means grouping the data with different relevant subsets of dimensions. This technique has become very effective as a distance measure becomes ineffective in a high dimensional space. This chapter presents a novel evolutionary approach to a bottom up subspace clustering SUBSPACE_DE which is scalable to high dimensional data. SUBSPACE_DE uses a self-adaptive DBSCAN algorithm to perform clustering in data instances of each attribute and maximal subspaces. Self-adaptive DBSCAN clustering algorithms accept input from differential evolution algorithms. The proposed SUBSPACE_DE algorithm is tested on 14 datasets, both real and synthetic. It is compared with 11 existing subspace clustering algorithms. Evaluation metrics such as F1_Measure and accuracy are used. Performance analysis of the proposed algorithms is considerably better on a success rate ratio ranking in both accuracy and F1_Measure. SUBSPACE_DE also has potential scalability on high dimensional datasets.

A feature group weighting method for subspace clustering of high-dimensional data

2012 ◽  
Vol 45 (1) ◽  
pp. 434-446 ◽  
Author(s):  
Xiaojun Chen ◽  
Yunming Ye ◽  
Xiaofei Xu ◽  
Joshua Zhexue Huang
Keyword(s):  
High Dimensional Data ◽  
Subspace Clustering ◽  
High Dimensional ◽  
Weighting Method ◽  
Feature Group

scholarly journals Automatic subspace clustering of high dimensional data for data mining applications

1998 ◽  
Vol 27 (2) ◽  
pp. 94-105 ◽  
Author(s):  
Rakesh Agrawal ◽  
Johannes Gehrke ◽  
Dimitrios Gunopulos ◽  
Prabhakar Raghavan
Keyword(s):  
Data Mining ◽  
High Dimensional Data ◽  
Subspace Clustering ◽  
High Dimensional

scholarly journals A Preview on Subspace Clustering of High Dimensional Data

2013 ◽  
Vol 6 (3) ◽  
pp. 441-448 ◽  
Author(s):  
Sajid Nagi ◽  
Dhruba Kumar Bhattacharyya ◽  
Jugal K. Kalita
Keyword(s):  
Search Strategy ◽  
High Dimensional Data ◽  
Subspace Clustering ◽  
High Dimensional ◽  
Expression Data ◽  
Clustering Methods ◽  
Top Down ◽  
Data Points ◽  
Low Dimensional ◽  
Entire Dataset

When clustering high dimensional data, traditional clustering methods are found to be lacking since they consider all of the dimensions of the dataset in discovering clusters whereas only some of the dimensions are relevant. This may give rise to subspaces within the dataset where clusters may be found. Using feature selection, we can remove irrelevant and redundant dimensions by analyzing the entire dataset. The problem of automatically identifying clusters that exist in multiple and maybe overlapping subspaces of high dimensional data, allowing better clustering of the data points, is known as Subspace Clustering. There are two major approaches to subspace clustering based on search strategy. Top-down algorithms find an initial clustering in the full set of dimensions and evaluate the subspaces of each cluster, iteratively improving the results. Bottom-up approaches start from finding low dimensional dense regions, and then use them to form clusters. Based on a survey on subspace clustering, we identify the challenges and issues involved with clustering gene expression data.

Sign in / Sign up

Export Citation Format

Share Document