Compressed hierarchical clustering

Gilad Baruch; Dana Shapira; Shmuel T. Klein

doi:10.1109/dcc.2018.00052

Compressed hierarchical clustering

Gilad Baruch
, Dana Shapira
, Shmuel T. Klein

Department of Computer Science and Artificial Intelligence

Ariel University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Hierarchical Clustering is widely used in Machine Learning and Data Mining. It stores bit-vectors in the nodes of a k-ary tree, usually without trying to compress them. We suggest a double usage of the {\sf xor}ing operations defining the Hamming distance used in the clustering process, extending it also to be used to transform the vector in one node into a more compressible form, as a function of the vector in the parent node. Compression is then achieved by run-length encoding, followed by optional Huffman coding, and we show how the compressed file may be processed directly, without decompression.

Original language	English
Title of host publication	Proceedings - DCC 2018
Subtitle of host publication	2018 Data Compression Conference
Editors	Ali Bilgin, James A. Storer, Joan Serra-Sagrista, Michael W. Marcellin
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	399
Number of pages	1
ISBN (Electronic)	9781538648834
DOIs	https://doi.org/10.1109/dcc.2018.00052
State	Published - 19 Jul 2018
Event	2018 Data Compression Conference, DCC 2018 - Snowbird, United States Duration: 27 Mar 2018 → 30 Mar 2018

Publication series

Name	Data Compression Conference Proceedings
Volume	2018-March
ISSN (Print)	1068-0314

Conference

Conference	2018 Data Compression Conference, DCC 2018
Country/Territory	United States
City	Snowbird
Period	27/03/18 → 30/03/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Keywords

Hamming distance
Hierarchical Clustering
Run length encoding

Access to Document

10.1109/dcc.2018.00052

Cite this

Baruch, G., Shapira, D., & Klein, S. T. (2018). Compressed hierarchical clustering. In A. Bilgin, J. A. Storer, J. Serra-Sagrista, & M. W. Marcellin (Eds.), Proceedings - DCC 2018: 2018 Data Compression Conference (pp. 399). (Data Compression Conference Proceedings; Vol. 2018-March). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/dcc.2018.00052

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title = "Compressed hierarchical clustering",

abstract = "Hierarchical Clustering is widely used in Machine Learning and Data Mining. It stores bit-vectors in the nodes of a k-ary tree, usually without trying to compress them. We suggest a double usage of the \{\textbackslash{}sf xor\}ing operations defining the Hamming distance used in the clustering process, extending it also to be used to transform the vector in one node into a more compressible form, as a function of the vector in the parent node. Compression is then achieved by run-length encoding, followed by optional Huffman coding, and we show how the compressed file may be processed directly, without decompression.",

keywords = "Hamming distance, Hierarchical Clustering, Run length encoding",

author = "Gilad Baruch and Dana Shapira and Klein, \{Shmuel T.\}",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 Data Compression Conference, DCC 2018 ; Conference date: 27-03-2018 Through 30-03-2018",

year = "2018",

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day = "19",

doi = "10.1109/dcc.2018.00052",

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series = "Data Compression Conference Proceedings",

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booktitle = "Proceedings - DCC 2018",

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Baruch, G, Shapira, D & Klein, ST 2018, Compressed hierarchical clustering. in A Bilgin, JA Storer, J Serra-Sagrista & MW Marcellin (eds), Proceedings - DCC 2018: 2018 Data Compression Conference. Data Compression Conference Proceedings, vol. 2018-March, Institute of Electrical and Electronics Engineers Inc., pp. 399, 2018 Data Compression Conference, DCC 2018, Snowbird, United States, 27/03/18. https://doi.org/10.1109/dcc.2018.00052

Compressed hierarchical clustering. / Baruch, Gilad; Shapira, Dana; Klein, Shmuel T.
Proceedings - DCC 2018: 2018 Data Compression Conference. ed. / Ali Bilgin; James A. Storer; Joan Serra-Sagrista; Michael W. Marcellin. Institute of Electrical and Electronics Engineers Inc., 2018. p. 399 (Data Compression Conference Proceedings; Vol. 2018-March).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - Hierarchical Clustering is widely used in Machine Learning and Data Mining. It stores bit-vectors in the nodes of a k-ary tree, usually without trying to compress them. We suggest a double usage of the {\sf xor}ing operations defining the Hamming distance used in the clustering process, extending it also to be used to transform the vector in one node into a more compressible form, as a function of the vector in the parent node. Compression is then achieved by run-length encoding, followed by optional Huffman coding, and we show how the compressed file may be processed directly, without decompression.

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Compressed hierarchical clustering

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Keywords

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