K-means Clustering Algorithm for Categorical Attributes

Gupta, Shyam; Rao, K. Seshagiri; Bhatnagar, Vasudha

doi:10.1007/3-540-48298-9_22

Cited by 53 publications

(32 citation statements)

References 1 publication

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“…In [11,12], the authors conduct a clustering analysis with binary data. Two individuals should be viewed as similar to the degree that they share a common pattern of attributes among the binary variables.…”

Section: A Clustering Large Dataset: State Of the Artmentioning

confidence: 99%

Pattern Recognition Approach in Multidimensional Databases: Application to the Global Terrorism Database

Salem¹,

Naouali²

2016

ijacsa

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Abstract-This paper presents a pattern recognition approach in multidimensional databases. The approach is based on a clustering method using the distance measurement between a reference profile and the database observations. Two distance measurements will be proposed: an adaptation of the Khi 2 formula to the multidimensional context, extracted from the Multiple Correspondence Analysis (MCA), and the Euclidean distance. A comparison between the two distances will be provided to retain the most efficient one for the multidimensional clustering context. The proposed approach will be applied to a real case study representing armed attacks worldwide stored in the Global Terrorism Database (GTD).

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Section: A Clustering Large Dataset: State Of the Artmentioning

confidence: 99%

Pattern Recognition Approach in Multidimensional Databases: Application to the Global Terrorism Database

Salem¹,

Naouali²

2016

ijacsa

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“…al. [15] to apply the K-means algorithm by adopting two different similarity measures. An integrated cost function is suggested which has two components.…”

Section: The Modified K -Means Algorithmsmentioning

confidence: 99%

“…al. [15] has neither been justified by mathematical means nor has it been validated by sufficient numerical testing. In addition, the method is not parameter-free.…”

Section: The Modified K -Means Algorithmsmentioning

confidence: 99%

More work on K -Means Clustering Algorithm: The Dimensionality Problem

BarisiBaridam¹

2012

IJCA

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The K-means clustering algorithm is an old algorithm that has been intensely researched owing to its simplicity of implementation. However, there have also been criticisms on its performance, in particular, for demanding the value of K a priori. It is evident from previous researches that providing the number of clusters a priori does not in any way assist in the production of good quality clusters. The objective of this paper is to investigate the usefulness of the K-means clustering in the clustering of high and multi-dimensional data by applying it to biological sequence data which is known for high and multi-dimension. The squared-Euclidean distance and the cosine measure are used as the similarity measures. The silhouette validity index is used first to show K-means algorithm"s inefficiency in the clustering of high and multidimensional data irrespective of the distance or similarity measure employed. A further study was to introduce a preprocessor scheme to the K-means algorithm to automatically initialize a suitable value of K prior to the execution of the K-mean algorithm. The dimensionality problem investigated suggests that the use of the preprocessor improves the quality of clusters significantly for the biological data sets considered. Furthermore, it is then shown that the Kmeans algorithm with preprocessor produces good quality, compact and well-separated clusters of the biological data obtained from a high-dimension-to-low-dimension mapping scheme introduced in the paper. General TermsK means, Clustering, Algorithm.

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“…However, in the categorical domain, there is no common way to decide cluster representative. Based on the assumption, the numerical data are converted into categorical data [22] as described below: [18].…”

Section: Categorical Fingerprint Feature Representationmentioning

confidence: 99%

Efficient Fingerprint Recognition Through Improvement of Feature Level Clustering, Indexing and Matching Using Discrete Cosine Transform

Indradevi¹

2011

Biometric Systems, Design and Applications

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K-means Clustering Algorithm for Categorical Attributes

Cited by 53 publications

References 1 publication

Pattern Recognition Approach in Multidimensional Databases: Application to the Global Terrorism Database

Pattern Recognition Approach in Multidimensional Databases: Application to the Global Terrorism Database

More work on K -Means Clustering Algorithm: The Dimensionality Problem

Efficient Fingerprint Recognition Through Improvement of Feature Level Clustering, Indexing and Matching Using Discrete Cosine Transform

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