A k-mean clustering algorithm for mixed numeric and categorical data

Ahmad, Amir; Dey, Lipika

doi:10.1016/j.datak.2007.03.016

Cited by 577 publications

(379 citation statements)

References 32 publications

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“…Literature review reveals the fact that most commonly used data mining techniques used for the analysis of mixed data is clustering [1,2,17,20,22,23]. In clustering, the use of hierarchical clustering technique has shown good results [3,19].…”

Section: Numeric and Nominal Data Analysismentioning

confidence: 99%

“…Specially, data sets with a large number of nominal variables, including some with large number of distinct values are becoming increasingly common [3]. For the purpose of efficient analysis of mixed data sets, [2] identified the problems associated with the traditional k-mean algorithm as best suited for numeric data only. In order to perform analysis on mixed data, the authors proposed a new algorithm which uses a cost function and distance measure based on co-occurrence of values.…”

Section: Numeric and Nominal Data Analysismentioning

confidence: 99%

“…Continuously increasing data repositories can contribute significantly towards future decision making provided appropriate knowledge discovery mechanisms are applied for extracting hidden, but potentially useful information embedded in the data [2]. One of the main mechanisms of knowledge discovery is the efficient analysis of data using modern analytical systems.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Integration of Data Mining and Data Warehousing: A Practical Methodology

Usman¹,

Pears²

2010

IJACT

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Section: Numeric and Nominal Data Analysismentioning

confidence: 99%

Section: Numeric and Nominal Data Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Integration of Data Mining and Data Warehousing: A Practical Methodology

Usman¹,

Pears²

2010

IJACT

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“…Metrics for mixed numerical and categorical values can be found in the literature (Ahmad and Dey, 2007;Gibert and Cortés, 1997). However, authors are not aware of references including also semantic features.…”

Section: Object Comparison With Numerical Categorical and Semantic Fmentioning

confidence: 99%

SEMANTIC CLUSTERING BASED ON ONTOLOGIES - An Application to the Study of Visitors in a Natural Reserve

Batet

Valls

Gibert

2011

Proceedings of the 3rd International Conference on Agents and Artificial Intelligence

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Abstract:The development of large ontologies for general and specific domains provides new tools to improve the quality of data mining techniques such as clustering. In this paper we explain how to improve clustering results by exploiting the semantics of categorical data by means of ontologies and how this semantics can be included into a hierarchical clustering method. We want to prove that when the conceptual meaning of the values is taken into account, it is possible to find a better interpretation of the clusters. This is demonstrated with the analysis of real data collected from visitors to of a Natural Reserve. The results of our methodology are compared with the ones obtained with a classical multivariate analysis done in the same database.

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“…Either the categorical attributes are codified [16], or the numerical ones are discretized to be considered as a category [15]. In the second approach, a common approximation is to use a similarity measure that considers each type of attribute in a different one way [1], [17] .…”

Section: Introductionmentioning

confidence: 99%

Geometrical codification for clustering mixed categorical and numerical databases

Barceló-Rico

Díez

2011

J Intell Inf Syst

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This paper presents an alternative to cluster mixed databases. The main idea is to propose a general method to cluster mixed data sets, which is not very complex and still can reach similar levels of performance of some good algorithms. The proposed approach is based on codifying the categorical attributes and use a numerical clustering algorithm on the resulting database.The codification proposed is based on polar or spherical coordinates, it is easy to understand and to apply, the increment in the length of the input matrix is not excessively large, and the codification error can be determined for each case.The proposed codification combined with the well known k-means algorithm showed a very good performance in different benchmarks and has been compared with both, other codifications and other mixed clustering algorithms, showing a better or comparable performance in all cases.

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A k-mean clustering algorithm for mixed numeric and categorical data

Cited by 577 publications

References 32 publications

Integration of Data Mining and Data Warehousing: A Practical Methodology

Integration of Data Mining and Data Warehousing: A Practical Methodology

SEMANTIC CLUSTERING BASED ON ONTOLOGIES - An Application to the Study of Visitors in a Natural Reserve

Geometrical codification for clustering mixed categorical and numerical databases

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