MK-means - Modified K-means clustering algorithm

DEE Group Author; Maria Rita Sarmento de Almeida Ribeiro

doi:10.1109/IJCNN.2010.5596300

MK-means - Modified K-means clustering algorithm

DEE Group Author, Maria Rita Sarmento de Almeida Ribeiro

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

Abstract

This paper discusses a density based clustering approach for a guided kernel based clustering algorithm, named MK-means (Modified K-means). Our idea is to improve the guided K-Means clustering algorithm and discuss the benefits of using MK-Means algorithm for clustering algorithm in astrophysics data bases. The improvements made allow handling clustering without apriori knowledge and also include the flexibility of merging classes when similarities are detected.

Original language	Unknown
Title of host publication	IEEE International Joint Conference on Neural Networks (IJCNN)
Pages	1-6
DOIs	https://doi.org/10.1109/IJCNN.2010.5596300
Publication status	Published - 1 Jan 2010
Event	IEEE World Congress on Computational Intelligence (WCCI 2010) - Duration: 1 Jan 2010 → …

Conference

Conference	IEEE World Congress on Computational Intelligence (WCCI 2010)
Period	1/01/10 → …

Access to Document

10.1109/IJCNN.2010.5596300

http://oa.uninova.pt/4786/

Cite this

@inproceedings{467ca48fd8104fa6a65b3b04812bbe66,

title = "MK-means - Modified K-means clustering algorithm",

abstract = "This paper discusses a density based clustering approach for a guided kernel based clustering algorithm, named MK-means (Modified K-means). Our idea is to improve the guided K-Means clustering algorithm and discuss the benefits of using MK-Means algorithm for clustering algorithm in astrophysics data bases. The improvements made allow handling clustering without apriori knowledge and also include the flexibility of merging classes when similarities are detected.",

author = "{DEE Group Author} and Ribeiro, {Maria Rita Sarmento de Almeida}",

year = "2010",

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doi = "10.1109/IJCNN.2010.5596300",

language = "Unknown",

isbn = "978-1-4244-6917-8",

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note = "null ; Conference date: 01-01-2010",

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AB - This paper discusses a density based clustering approach for a guided kernel based clustering algorithm, named MK-means (Modified K-means). Our idea is to improve the guided K-Means clustering algorithm and discuss the benefits of using MK-Means algorithm for clustering algorithm in astrophysics data bases. The improvements made allow handling clustering without apriori knowledge and also include the flexibility of merging classes when similarities are detected.

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DO - 10.1109/IJCNN.2010.5596300

M3 - Conference contribution

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BT - IEEE International Joint Conference on Neural Networks (IJCNN)

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