Comparison of K-means clustering initialization approaches with brute-force initialization

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F17%3A10236274" target="_blank" >RIV/61989100:27240/17:10236274 - isvavai.cz</a>

Alternative codes found

RIV/61989100:27740/17:10236274

Result on the web

<a href="https://link.springer.com/content/pdf/10.1007%2F978-981-10-3409-1_7.pdf" target="_blank" >https://link.springer.com/content/pdf/10.1007%2F978-981-10-3409-1_7.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-981-10-3409-1_7" target="_blank" >10.1007/978-981-10-3409-1_7</a>

Result language

angličtina

Original language name

Comparison of K-means clustering initialization approaches with brute-force initialization

Original language description

Data clustering is a basic data mining discipline that has been in center of interest of many research groups. This paper describes the formulation of the basic NP-hard optimization problem in data clustering which is approximated by many heuristic methods. The famous k-means clustering algorithm and its initialization is of a particular interest in this paper. A summary of the k-means variants and various initialization strategies is presented. Many initialization heuristics tend to search only through a fraction of the initial centroid space. The final clustering result is usually compared only to some other heuristic strategy. In this paper we compare the result to the solution provided by a brute-force experiment. Many instances of the k-means can be executed in parallel on the high performance computing infrastructure, which makes brute-force search for the best initial centroids possible. Solutions obtained by exact solvers [2, 11] of the clustering problem are used for verification of the brute-force approach. We present progress of the function optimization during the experiment for several benchmark data sets, including sparse document-term matrices. © Springer Nature Singapore Pte Ltd. 2017.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/LQ1602" target="_blank" >LQ1602: IT4Innovations excellence in science</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year

2017

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Advances in Intelligent Systems and Computing. Volume 567

ISBN

978-981-10-3408-4

ISSN

2194-5357

e-ISSN

neuvedeno

Number of pages

12

Pages from-to

103-114

Publisher name

Springer

Place of publication

Singapur

Event location

Kalkata

Event date

Aug 12, 2016

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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