IK-BKM: An incremental clustering approach based on intra-cluster distance

Hariz, Sarra Ben; Elouedi, Zied

doi:10.1109/aiccsa.2010.5587008

Cited by 8 publications

(4 citation statements)

References 16 publications

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“…To our knowledge, only one incremental approach to clustering using belief functions has been proposed [2]. However, in this approach the number of clusters is known in advance so this is not adapted for online applications.…”

Section: Resultsmentioning

confidence: 99%

E2GK: Evidential Evolving Gustafsson-Kessel Algorithm for Data Streams Partitioning Using Belief Functions

Serir

Ramasso

Zerhouni

2011

Lecture Notes in Computer Science

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A new online clustering method, called E2GK (Evidential Evolving Gustafson-Kessel) is introduced in the theoretical framework of belief functions. The algorithm enables an online partitioning of data streams based on two existing and efficient algorithms: Evidantial c-Means (ECM) and Evolving Gustafson-Kessel (EGK). E2GK uses the concept of credal partition of ECM and adapts EGK, offering a better interpretation of the data structure. Experiments with synthetic data sets show good performances of the proposed algorithm compared to the original online procedure.

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Section: Resultsmentioning

confidence: 99%

E2GK: Evidential Evolving Gustafsson-Kessel Algorithm for Data Streams Partitioning Using Belief Functions

Serir

Ramasso

Zerhouni

2011

Lecture Notes in Computer Science

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“…The words which belong to different clusters will have a different context. The distance between the words belonging to the same cluster is known as intra-cluster distance [7]. This intra-cluster distance will always be less.…”

Section: Implementation Details Identify New Cluster Centroid By Compmentioning

confidence: 99%

“…Keep these #campfire #safety tips in mind during ", [4] "rhettandlink talk about their visit to Aussie and accidental HCFC cameo!\n\nOriginally shared by @Bluzae ", [5] "The accidental hilarity of the self own is just too perfect.\nCohen worked for Trump for over a decade. ", [6] "Congressman Louis T. MacFadden, Chairman of the House Banking & Currency Committee: It was not accidental", [7] "There is Nothing Accidental About These Threats, It Is a Pattern: T.M.Krishna", [8] "I met her accidentally and it was all fun"…”

Section: Test Casementioning

confidence: 99%

Research and Optimization of Data Classification using K-means Clustering and Affinity Propagation Technique

2019

IJITEE

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Amongst various social networking platforms available in this digital millennium, Twitter facilitates a huge platform to accomplish analysis on data with respect to trends, events, personalities etc. Twitter facilitates the analysts in fetching essential information of the population based on their likes and preferences. Clustering technique is one of the prominent techniques available to fetch the essential data from the massive data being populated. Several clustering methods are available to achieve the objective of grouping the data. This paper throws light on the performance and efficiency of several algorithms used in determining the trending pulses effectively. The clusters of data obtained after clustering are further subjected to classification based on the topics for real time analysis. This paper discusses the flaws obtained in the classification of the data. The data is again subjected to an optimized classification technique and analyzed against the clusters of data.

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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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IK-BKM: An incremental clustering approach based on intra-cluster distance

Cited by 8 publications

References 16 publications

E2GK: Evidential Evolving Gustafsson-Kessel Algorithm for Data Streams Partitioning Using Belief Functions

E2GK: Evidential Evolving Gustafsson-Kessel Algorithm for Data Streams Partitioning Using Belief Functions

Research and Optimization of Data Classification using K-means Clustering and Affinity Propagation Technique

Categorical Data Clustering: A Bibliometric Analysis and Taxonomy

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