A Medoid-based Method for Clustering Categorical Data

Seman, Ali; Bakar, Zainab Abu; Sapawi, Azizian Mohd; Othman, Ida Rosmini

doi:10.3923/jai.2013.257.265

Cited by 7 publications

(5 citation statements)

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“…In general, the new initial center selection method and the new dominant weighting method introduced by Seman et al (2015) were not restricted by highly similar categorical data (such as Y-STR data (Seman et al, 2013a) that had previously been applied and reported by Seman et al (2010aSeman et al ( , b, c, d, e, f, 2012aSeman et al ( , b, 2015. Meanwhile, the applications of these methods can also be extended for use with common categorical data and reported with promising results, particularly for a high number of clusters such as the seven clusters of zoo data set (Seman et al, 2013b). In addition, for the new κ-AMH-type algorithm represented by Nκ-AMH I, which combines the new initial center selection method with the original dominant weighting Res.…”

Section: Discussionmentioning

confidence: 99%

“…Notably, all κ-AMH-type algorithms can be used to further develop clustering tools. This is because κ-AMH-type algorithms, characterized by the use of the object as the center cluster and highlighted in Seman et al (2013b), have their own advantages over κ-Mode-type algorithms (e.g., the κ-mode (Huang, 1998), fuzzy κ-mode algorithms (Huang and Ng, 1999) and new fuzzy κ-mode algorithms (Ng and Jing, 2009), which are characterized by the mode mechanism.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, the κ-AMH algorithm was also tested for clustering other categorical data sets-e.g., soybean, voting, breast, zoo, mushroom, lymphography, credit, hepatitis and adults with promising results (Seman et al, 2013b). In fact, in some cases (e.g., for clustering the zoo data set with a higher number of clusters (seven clusters)), the κ-AMH algorithm was more accurate than κ-Mode-type algorithms such as the fuzzy κ-Modes and the new fuzzy κ-Modes algorithm.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Performance Evaluations of κ-Approximate Modal Haplotype Type Algorithms for Clustering Categorical Data

Seman¹,

Sapawi²,

Salleh³

2015

Research J. of Information Technology

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The effectiveness of the performance of κ-Approximate Modal Haplotype (κ-AMH)-type algorithms for clustering Y-short tandem repeats (Y-STR) of categorical data has been demonstrated previously. However, newly introduced κ-AMH-type algorithms, including the new κ-AMH I (Nκ-AMH 1), the new κ-AMH II (Nκ-AMH II) and the new κ-AMH III (Nκ-AMH III), are derived from the same κ-AMH optimization and fuzzy procedures but with the inclusion of two new methods, namely, new initial center selection and new dominant weighting methods. This study evaluates and presents the performance of κ-AMH-type algorithms for clustering five categorical data sets-namely, soybean, zoo, hepatitis, voting and breast. The performance criteria include accuracy, precision and recall analyses. Overall, κ-AMH-type algorithms perform well when clustering all of the categorical data sets mentioned above. Specifically, the N κ-AMH I algorithm exhibits the best performance when clustering the five categorical data sets; this algorithm obtained the highest combined mean accuracy score (at 0.9130), compared to those of κ-AMH (0.8971), N κ-AMH II (0.8885) and N κ-AMH III (0.9011). This high score is associated with the newly introduced initial center selection, combined with the original dominant weighting method. These results present a new and significant benchmark, indicating that κ-AMH-type algorithms can be generalized for any categorical data.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Performance Evaluations of κ-Approximate Modal Haplotype Type Algorithms for Clustering Categorical Data

Seman¹,

Sapawi²,

Salleh³

2015

Research J. of Information Technology

Self Cite

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“…Clustering methods have been highlighted in many research and applied in many domains [9][10][11][12][13]. In clustering the idea is not to predict the target class as like classification, it is more ever trying to group the similar kind of things by considering the most satisfied conditions all the items in the same group should be similar and no two different group items should not be similar [14].…”

Section: Clustering Methodsmentioning

confidence: 99%

Evaluation of Clustering Methods for Student Learning Style Based Neuro Linguistic Programming

Yusoff¹,

Fathi

2018

IJET

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Students' performance is a key point to get a better first impression during a job interview with an employer. However, there are several factors, which affect students' performances during their study. One of them is their learning style, which is under Neurolinguistic Programming (NLP) approach. Learning style is divided into a few behavioral categories, Visual, Auditory and Kinesthetics (VAK). This paper addresses the evaluation of clustering methods for the identification of learning style based on system preferences. It starts with the distribution of questionnaires to acquire the information on the VAK for each student. About 167 respondents in the Faculty of Computer and Mathematical Science are collected. It is then pre-processed to prepare the data for clustering method evaluations. Three clustering methods; Simple K-Mean, Hierarchical and Density-Based Spatial Clustering of Applications with Noise are evaluated. The findings show that Simple K-Mean offers the most accurate prediction. Upon completion, by using the dataset, Simple K-Means technique estimated four clusters that yield the highest accuracy of 74.85 % compared to Hierarchical Clustering, which estimated four clusters and Density-Based Spatial Clustering of Applications with Noise which estimated three clusters with 52.69% and 61.68 % respectively. The clustering method demonstrates the capability of categorizing the learning style of students based on three categories; visual, auditory and kinesthetic. This outcome would be beneficial to lecturers or teachers in university and school with an automatically clustering the students' learning style and would assist them in teaching and learning, respectively.

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“…Recently, a new algorithm called the k-Approximate Modal Haplotype (k-AMH) algorithm, which manipulates objects as the center of clusters, was exclusively introduced for clustering categorical values, particularly the DNA datasets of Y-Short Tandem Repeats (Y-STR) (Seman et al, 2012a). The k-AMH algorithm comes with its own medoid mechanism as the center of its clusters and has proven to be efficient in clustering Y-STR (Seman et al, 2012b) and other categorical data, such as soybean and voting (Seman et al, 2013). The algorithm relies on the maximization of its cost function and employs the fuzzy clustering framework as incorporated by the fuzzy k-means-type and fuzzy k-modes-type algorithms.…”

Section: Introductionmentioning

confidence: 99%

Extensions to the K-Amh Algorithm for Numerical Clustering

Seman

Sapawi

2018

JICT

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The k-AMH algorithm has been proven efficient in clustering categorical datasets. It can also be used to cluster numerical values with minimum modification to the original algorithm. In this paper, we present two algorithms that extend the k-AMH algorithm to the clustering of numerical values. The original k-AMH algorithm for categorical values uses a simple matching dissimilarity measure, but for numerical values it uses Euclidean distance. The first extension to the k-AMH algorithm, denoted k-AMH Numeric I, enables it to cluster numerical values in a fashion similar to k-AMH for categorical data. The second extension, k-AMH Numeric II, adopts the cost function of the fuzzy k-Means algorithm together with Euclidean distance, and has demonstrated performance similar to that of k-AMH Numeric I. The clustering performance of the two algorithms was evaluated on six real-world datasets against a benchmark algorithm, the fuzzy k-Means algorithm. The results obtained indicate that the two algorithms are as efficient as the fuzzy k-Means algorithm when clustering numerical values. Further, on an ANOVA test, k-AMH Numeric I obtained the highest accuracy score of 0.69 for the six datasets combined with p-value less than 0.01, indicating a 95% confidence level. The experimental results prove that the k-AMH Numeric I and k-AMH Numeric II algorithms can be

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A Medoid-based Method for Clustering Categorical Data

Cited by 7 publications

References 5 publications

Performance Evaluations of κ-Approximate Modal Haplotype Type Algorithms for Clustering Categorical Data

Performance Evaluations of κ-Approximate Modal Haplotype Type Algorithms for Clustering Categorical Data

Evaluation of Clustering Methods for Student Learning Style Based Neuro Linguistic Programming

Extensions to the K-Amh Algorithm for Numerical Clustering

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