DK-BKM: Decremental K Belief K-Modes Method

Hariz, Sarra Ben; Elouedi, Zied

doi:10.1007/978-3-642-15951-0_13

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…They have been combined with clustering techniques to cluster uncertain data. Some of the prominent theories include the fuzzy sets [1], [2], the belief function theory [3], [4], [5] the rough sets [6], [7], [8] and the possibility theory [9], [10], [11].…”

Section: Introductionmentioning

confidence: 99%

The k-modes method using possibility and rough set theories

Ammar

Elouedi

Lingras

2013

2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS)

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This paper investigates rough clustering of objects from uncertain databases using possibility and rough set theories. Real databases can contain both certain and uncertain attribute values. To properly cluster such instances into different clusters, it is necessary to take into account such uncertainty. When clustering objects with uncertain values, we have to consider the similarities between each object and all clusters in order to provide more accurate clustering results. To this end, we propose a new approach based on the k-modes method to cluster categorical objects using possibility and rough set theories to deal with uncertainty. First, possibility theory is applied to handle uncertain attribute values of instances and to specify membership degrees of each instance to all clusters. Then, rough set theory is used to detect peripheral objects and to form clusters with rough boundaries. The results of the proposed approach compare favourably with other certain and uncertain approaches based on different evaluation criteria.

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Section: Introductionmentioning

confidence: 99%

The k-modes method using possibility and rough set theories

Ammar

Elouedi

Lingras

2013

2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS)

Self Cite

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“…Unlike BKM, which maintains a fixed number of clusters, objects, and features, BCDP considers the uncertainty of attribute values and the potential adjustment of cluster numbers using the concepts of cluster cohesion and separation concepts. This adjustment can involve either increasing (IK-BKM) [139] or decreasing (DK-BKM) [140] the number of clusters. As a result, the partitioning of clusters is updated without requiring a complete re-clustering process from scratch.…”

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confidence: 99%

Categorical Data Clustering: A Bibliometric Analysis and Taxonomy

Cendana,

Kuo

2024

MAKE

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Numerous real-world applications apply categorical data clustering to find hidden patterns in the data. The K-modes-based algorithm is a popular algorithm for solving common issues in categorical data, from outlier and noise sensitivity to local optima, utilizing metaheuristic methods. Many studies have focused on increasing clustering performance, with new methods now outperforming the traditional K-modes algorithm. It is important to investigate this evolution to help scholars understand how the existing algorithms overcome the common issues of categorical data. Using a research-area-based bibliometric analysis, this study retrieved articles from the Web of Science (WoS) Core Collection published between 2014 and 2023. This study presents a deep analysis of 64 articles to develop a new taxonomy of categorical data clustering algorithms. This study also discusses the potential challenges and opportunities in possible alternative solutions to categorical data clustering.

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Ranking-Based Feature Selection Method for Dynamic Belief Clustering

Hariz

Elouedi

2011

Adaptive and Intelligent Systems

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DK-BKM: Decremental K Belief K-Modes Method

Cited by 5 publications

References 13 publications

The k-modes method using possibility and rough set theories

The k-modes method using possibility and rough set theories

Categorical Data Clustering: A Bibliometric Analysis and Taxonomy

Ranking-Based Feature Selection Method for Dynamic Belief Clustering

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