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Joint Feature Learning and Clustering Techniques for Clustering High Dimensional Data: A Review

Ghatage Trupti B.1 , Patil Deepali E.2 , Takmare Sachin B.3 , Patil Sushama A.4

Section:Review Paper, Product Type: Journal Paper
Volume-4 , Issue-3 , Page no. 54-58, Mar-2016

Online published on Mar 30, 2016

Copyright © Ghatage Trupti B., Patil Deepali E., Takmare Sachin B., Patil Sushama A. . 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: Ghatage Trupti B., Patil Deepali E., Takmare Sachin B., Patil Sushama A., “Joint Feature Learning and Clustering Techniques for Clustering High Dimensional Data: A Review,” International Journal of Computer Sciences and Engineering, Vol.4, Issue.3, pp.54-58, 2016.

MLA Style Citation: Ghatage Trupti B., Patil Deepali E., Takmare Sachin B., Patil Sushama A. "Joint Feature Learning and Clustering Techniques for Clustering High Dimensional Data: A Review." International Journal of Computer Sciences and Engineering 4.3 (2016): 54-58.

APA Style Citation: Ghatage Trupti B., Patil Deepali E., Takmare Sachin B., Patil Sushama A., (2016). Joint Feature Learning and Clustering Techniques for Clustering High Dimensional Data: A Review. International Journal of Computer Sciences and Engineering, 4(3), 54-58.

BibTex Style Citation:
@article{B._2016,
author = {Ghatage Trupti B., Patil Deepali E., Takmare Sachin B., Patil Sushama A.},
title = {Joint Feature Learning and Clustering Techniques for Clustering High Dimensional Data: A Review},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {3 2016},
volume = {4},
Issue = {3},
month = {3},
year = {2016},
issn = {2347-2693},
pages = {54-58},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=827},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=827
TI - Joint Feature Learning and Clustering Techniques for Clustering High Dimensional Data: A Review
T2 - International Journal of Computer Sciences and Engineering
AU - Ghatage Trupti B., Patil Deepali E., Takmare Sachin B., Patil Sushama A.
PY - 2016
DA - 2016/03/30
PB - IJCSE, Indore, INDIA
SP - 54-58
IS - 3
VL - 4
SN - 2347-2693
ER -

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Abstract

In many real world applications, we often face high dimensional data. Developing efficient clustering methods for high dimensional datasets may be a challenging problem because of the curse of dimensionality. Common method to deal with this is to use first dimensionality reduction approach and then cluster the data in the lower dimensions. Even though we can initially reduce the dimensionality by any approach and then use clustering approaches to group high dimensional data, performance can also be improved since these two techniques are conducted in sequence. Naturally, if we consider the requirement of clustering during the process of dimensionality reduction and vice versus then the performance of clustering can be improved. This paper presents a review of different techniques for clustering high dimensional data by joint feature learning and clustering.

Key-Words / Index Term

Clustering, high dimensional data, feature learning, dimensionality reduction

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