Integrating apriori with paired k-means for cluster fixed mixed data

Haripriya Harikumar; Shaji Amrutha; R Veena; Prema Nedungadi

doi:10.1145/2791405.2791437

Integrating apriori with paired k-means for cluster fixed mixed data

Haripriya Harikumar, Shaji Amrutha, R Veena, Prema Nedungadi

Machine Learning and Robotics

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

Abstract

The field of data mining is concerned with finding interesting patterns from an unstructured data. A simple, popular as well as an efficient clustering technique for data analysis is k-means. But classical k-means algorithm can only be applied to numerical data where k is a user given value. But the data generated from a wide variety of domains are of mixed form and it is effortful to trust on a user given value for k. So our objective is to effectively use an association rule mining algorithm which can automatically compute the number of clusters and a pairwise distance measure for calculating the distance in mixed data. We have done experimentations with real mixed data taken from the UCI repository.

Original language	English
Title of host publication	WCI '15
Subtitle of host publication	Proceedings of the Third International Symposium on Women in Computing and Informatics
Editors	Indu Nair
Place of Publication	New York, NY
Publisher	Association for Computing Machinery
Pages	10-16
Number of pages	7
ISBN (Electronic)	9781450333610
DOIs	https://doi.org/10.1145/2791405.2791437
Publication status	Published - 10 Aug 2015
Event	3rd International Symposium on Women in Computing and Informatics - Kochi, India Duration: 10 Aug 2015 → 13 Aug 2015

Conference

Conference	3rd International Symposium on Women in Computing and Informatics
Abbreviated title	WCI '15
Country/Territory	India
City	Kochi
Period	10/08/15 → 13/08/15

Access to Document

10.1145/2791405.2791437

Cite this

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title = "Integrating apriori with paired k-means for cluster fixed mixed data",

abstract = "The field of data mining is concerned with finding interesting patterns from an unstructured data. A simple, popular as well as an efficient clustering technique for data analysis is k-means. But classical k-means algorithm can only be applied to numerical data where k is a user given value. But the data generated from a wide variety of domains are of mixed form and it is effortful to trust on a user given value for k. So our objective is to effectively use an association rule mining algorithm which can automatically compute the number of clusters and a pairwise distance measure for calculating the distance in mixed data. We have done experimentations with real mixed data taken from the UCI repository.",

author = "Haripriya Harikumar and Shaji Amrutha and R Veena and Prema Nedungadi",

year = "2015",

month = aug,

day = "10",

doi = "10.1145/2791405.2791437",

language = "English",

pages = "10--16",

editor = "Indu Nair",

booktitle = "WCI '15",

publisher = "Association for Computing Machinery",

address = "United States",

note = "3rd International Symposium on Women in Computing and Informatics, WCI '15 ; Conference date: 10-08-2015 Through 13-08-2015",

}

Harikumar, H, Amrutha, S, Veena, R & Nedungadi, P 2015, Integrating apriori with paired k-means for cluster fixed mixed data. in I Nair (ed.), WCI '15: Proceedings of the Third International Symposium on Women in Computing and Informatics. Association for Computing Machinery, New York, NY, pp. 10-16, 3rd International Symposium on Women in Computing and Informatics, Kochi, India, 10/08/15. https://doi.org/10.1145/2791405.2791437

Integrating apriori with paired k-means for cluster fixed mixed data. / Harikumar, Haripriya; Amrutha, Shaji; Veena, R et al.
WCI '15: Proceedings of the Third International Symposium on Women in Computing and Informatics. ed. / Indu Nair. New York, NY: Association for Computing Machinery, 2015. p. 10-16.

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

TY - GEN

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AU - Amrutha, Shaji

AU - Veena, R

AU - Nedungadi, Prema

PY - 2015/8/10

Y1 - 2015/8/10

N2 - The field of data mining is concerned with finding interesting patterns from an unstructured data. A simple, popular as well as an efficient clustering technique for data analysis is k-means. But classical k-means algorithm can only be applied to numerical data where k is a user given value. But the data generated from a wide variety of domains are of mixed form and it is effortful to trust on a user given value for k. So our objective is to effectively use an association rule mining algorithm which can automatically compute the number of clusters and a pairwise distance measure for calculating the distance in mixed data. We have done experimentations with real mixed data taken from the UCI repository.

AB - The field of data mining is concerned with finding interesting patterns from an unstructured data. A simple, popular as well as an efficient clustering technique for data analysis is k-means. But classical k-means algorithm can only be applied to numerical data where k is a user given value. But the data generated from a wide variety of domains are of mixed form and it is effortful to trust on a user given value for k. So our objective is to effectively use an association rule mining algorithm which can automatically compute the number of clusters and a pairwise distance measure for calculating the distance in mixed data. We have done experimentations with real mixed data taken from the UCI repository.

U2 - 10.1145/2791405.2791437

DO - 10.1145/2791405.2791437

M3 - Conference contribution

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EP - 16

BT - WCI '15

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T2 - 3rd International Symposium on Women in Computing and Informatics

Y2 - 10 August 2015 through 13 August 2015

ER -

Integrating apriori with paired k-means for cluster fixed mixed data

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