Applying Compression to Hierarchical Clustering

Gilad Baruch; Shmuel Tomi Klein; Dana Shapira

doi:10.1007/978-3-030-02224-2_12

Applying Compression to Hierarchical Clustering

Gilad Baruch, Shmuel Tomi Klein, Dana Shapira

Department of Computer Science

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

3 Scopus citations

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 data compression application of hierarchical clustering with a double usage of the xoring 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	Similarity Search and Applications - 11th International Conference, SISAP 2018, Proceedings
Editors	Stéphane Marchand-Maillet, Yasin N. Silva, Edgar Chávez
Pages	151-162
Number of pages	12
DOIs	https://doi.org/10.1007/978-3-030-02224-2_12
State	Published - 2018
Event	11th International Conference on Similarity Search and Applications, SISAP 2018 - Lima, Peru Duration: 7 Oct 2018 → 9 Oct 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11223 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	11th International Conference on Similarity Search and Applications, SISAP 2018
Country/Territory	Peru
City	Lima
Period	7/10/18 → 9/10/18

Access to Document

10.1007/978-3-030-02224-2_12

Cite this

Baruch, G., Klein, S. T., & Shapira, D. (2018). Applying Compression to Hierarchical Clustering. In S. Marchand-Maillet, Y. N. Silva, & E. Chávez (Eds.), Similarity Search and Applications - 11th International Conference, SISAP 2018, Proceedings (pp. 151-162). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11223 LNCS). https://doi.org/10.1007/978-3-030-02224-2_12

Baruch, Gilad ; Klein, Shmuel Tomi ; Shapira, Dana. / Applying Compression to Hierarchical Clustering. Similarity Search and Applications - 11th International Conference, SISAP 2018, Proceedings. editor / Stéphane Marchand-Maillet ; Yasin N. Silva ; Edgar Chávez. 2018. pp. 151-162 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{30a0b5fe75604eadbd6c1fa69fd87f67,

title = "Applying Compression to 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 data compression application of hierarchical clustering with a double usage of the xoring 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.",

author = "Gilad Baruch and Klein, {Shmuel Tomi} and Dana Shapira",

note = "Publisher Copyright: {\textcopyright} 2018, Springer Nature Switzerland AG.; 11th International Conference on Similarity Search and Applications, SISAP 2018 ; Conference date: 07-10-2018 Through 09-10-2018",

year = "2018",

doi = "10.1007/978-3-030-02224-2_12",

language = "אנגלית",

isbn = "9783030022235",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

pages = "151--162",

editor = "St{\'e}phane Marchand-Maillet and Silva, {Yasin N.} and Edgar Ch{\'a}vez",

booktitle = "Similarity Search and Applications - 11th International Conference, SISAP 2018, Proceedings",

}

Baruch, G, Klein, ST & Shapira, D 2018, Applying Compression to Hierarchical Clustering. in S Marchand-Maillet, YN Silva & E Chávez (eds), Similarity Search and Applications - 11th International Conference, SISAP 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11223 LNCS, pp. 151-162, 11th International Conference on Similarity Search and Applications, SISAP 2018, Lima, Peru, 7/10/18. https://doi.org/10.1007/978-3-030-02224-2_12

Applying Compression to Hierarchical Clustering. / Baruch, Gilad; Klein, Shmuel Tomi; Shapira, Dana.
Similarity Search and Applications - 11th International Conference, SISAP 2018, Proceedings. ed. / Stéphane Marchand-Maillet; Yasin N. Silva; Edgar Chávez. 2018. p. 151-162 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11223 LNCS).

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

TY - GEN

T1 - Applying Compression to Hierarchical Clustering

AU - Baruch, Gilad

AU - Klein, Shmuel Tomi

AU - Shapira, Dana

PY - 2018

Y1 - 2018

N2 - 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 data compression application of hierarchical clustering with a double usage of the xoring 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.

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 data compression application of hierarchical clustering with a double usage of the xoring 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.

UR - http://www.scopus.com/inward/record.url?scp=85055083865&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-02224-2_12

DO - 10.1007/978-3-030-02224-2_12

M3 - ???researchoutput.researchoutputtypes.contributiontobookanthology.conference???

AN - SCOPUS:85055083865

SN - 9783030022235

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 151

EP - 162

BT - Similarity Search and Applications - 11th International Conference, SISAP 2018, Proceedings

A2 - Marchand-Maillet, Stéphane

A2 - Silva, Yasin N.

A2 - Chávez, Edgar

T2 - 11th International Conference on Similarity Search and Applications, SISAP 2018

Y2 - 7 October 2018 through 9 October 2018

ER -

Baruch G, Klein ST, Shapira D. Applying Compression to Hierarchical Clustering. In Marchand-Maillet S, Silva YN, Chávez E, editors, Similarity Search and Applications - 11th International Conference, SISAP 2018, Proceedings. 2018. p. 151-162. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-02224-2_12

Applying Compression to Hierarchical Clustering

Abstract

Publication series

Conference

Access to Document

Other files and links

Fingerprint

Cite this