IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Subspace Clustering of High Dimensional Data Using Differential Evolution

Subspace Clustering of High Dimensional Data Using Differential Evolution
View Sample PDF
Author(s): Parul Agarwal (Jaypee Institute of Information Technology, India) and Shikha Mehta (Jaypee Institute of Information Technology, India)
Copyright: 2019
Pages: 28
Source title: Nature-Inspired Algorithms for Big Data Frameworks
Source Author(s)/Editor(s): Hema Banati (Dyal Singh College, India), Shikha Mehta (Jaypee Institute of Information Technology, India) and Parmeet Kaur (Jaypee Institute of Information Technology, India)
DOI: 10.4018/978-1-5225-5852-1.ch003

Purchase

View Subspace Clustering of High Dimensional Data Using Differential Evolution on the publisher's website for pricing and purchasing information.

Abstract

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.

Related Content

Junichiro Hayano, Emi Yuda. © 2021. 15 pages.
Anna Karagianni, Vasiliki Geropanta, Panagiotis Parthenios, Riccardo Porreca, Sofia Mavroudi, Antonios Vogiatzis, Lais-Ioanna Margiori, Christos Mpaknis, Eleutheria Papadosifou, Asimina Ioanna Sampani. © 2021. 21 pages.
Elias Munapo. © 2021. 16 pages.
Elias Munapo, Olusegun Sunday Ewemooje. © 2021. 16 pages.
Zakhid Godzhaev, Sergey Senkevich, Viktor Kuzmin, Izzet Melikov. © 2021. 19 pages.
Elias Munapo. © 2021. 22 pages.
Diriba Kajela Geleta, Mukhdeep Singh Manshahia. © 2021. 39 pages.
Body Bottom