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A Survival Study on Data Structure Based Clustering Techniques for Multidimensional Data Stream Analysis

K. Chitra1 , D. Maheswari2

  1. School of Computer Studies – PG, Rathnavel Subramaniam College of Arts and Science, Coimbatore, India.
  2. School of Computer Studies – PG, Rathnavel Subramaniam College of Arts and Science, Coimbatore, India.

Correspondence should be addressed to: chitra.k@rvsgroup.com.

Section:Research Paper, Product Type: Journal Paper
Volume-5 , Issue-12 , Page no. 101-108, Dec-2017

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v5i12.101108

Online published on Dec 31, 2017

Copyright © K. Chitra, D. Maheswari . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: K. Chitra, D. Maheswari, “A Survival Study on Data Structure Based Clustering Techniques for Multidimensional Data Stream Analysis,” International Journal of Computer Sciences and Engineering, Vol.5, Issue.12, pp.101-108, 2017.

MLA Style Citation: K. Chitra, D. Maheswari "A Survival Study on Data Structure Based Clustering Techniques for Multidimensional Data Stream Analysis." International Journal of Computer Sciences and Engineering 5.12 (2017): 101-108.

APA Style Citation: K. Chitra, D. Maheswari, (2017). A Survival Study on Data Structure Based Clustering Techniques for Multidimensional Data Stream Analysis. International Journal of Computer Sciences and Engineering, 5(12), 101-108.

BibTex Style Citation:
@article{Chitra_2017,
author = {K. Chitra, D. Maheswari},
title = {A Survival Study on Data Structure Based Clustering Techniques for Multidimensional Data Stream Analysis},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {12 2017},
volume = {5},
Issue = {12},
month = {12},
year = {2017},
issn = {2347-2693},
pages = {101-108},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=1587},
doi = {https://doi.org/10.26438/ijcse/v5i12.101108}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v5i12.101108}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=1587
TI - A Survival Study on Data Structure Based Clustering Techniques for Multidimensional Data Stream Analysis
T2 - International Journal of Computer Sciences and Engineering
AU - K. Chitra, D. Maheswari
PY - 2017
DA - 2017/12/31
PB - IJCSE, Indore, INDIA
SP - 101-108
IS - 12
VL - 5
SN - 2347-2693
ER -

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Abstract

Data mining plays an effective role in the field of computer science to analysis the data objects. The data mining process is used to mine the knowledge from huge database. Then, the extracted information is modified into an understandable data structure for the future analysis. The data structure in a computer is an essential approach to categorize and manage the data which is utilized for efficient usage. The data stream is referred as a structured sequence of instances; the data stream mining discovers the knowledge structures from continuous and fast data records. The clustering is the process of creating the group by collecting the data of similar patterns and also describes the meaningful structure of data. The additional process of traditional clustering termed as Subspace Clustering which is utilized for detecting the clusters in various subspaces within dataset. Then, the subspace clustering algorithms are introduced to discover the cluster in multiple overlapping subspaces by searching the relevant dimensions. Many research works are developed for managing the high dimensional data with the objective of providing better improvement on minimizing the performance of dimensionality and enhancing the clustering accuracy. However, the existing works failed to reduce the space complexity. Therefore, the research work focuses on reducing the dimensionality with improved clustering accuracy by executing the clustering and subspace clustering for data stream with data structure techniques.

Key-Words / Index Term

Data stream, Multidimensional data, Data mining, Data structure, Subspace clustering

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