A genetic fuzzy <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si218.gif" overflow="scroll"><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:math>-Modes algorithm for clustering categorical data

Gan, Guojun; Jian, Wu; Yang, Zhijing

doi:10.1016/j.eswa.2007.11.045

Cited by 90 publications

(30 citation statements)

References 14 publications

(16 reference statements)

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“…Also some of the objects have missing values, we denote the missing value by "?" and treat it as an additional category for that attribute [8]. These two categorical data sets are available at ftp://ftp.ics.uci.edu./pub/machine-learningdatabases/.…”

Section: B Data Setsmentioning

confidence: 99%

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Clustering categorical data using a swarm-based method

Izakian

Abraham

Snášel

2009

2009 World Congress on Nature &Amp; Biologically Inspired Computing (NaBIC)

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Abstract-The K-Modes algorithm is one of the most popular clustering algorithms in dealing with categorical data. But the random selection of starting centers in this algorithm may lead to different clustering results and falling into local optima. In this paper we proposed a swarm-based K-Modes algorithm. The experimental results over two well known Soybean and Congressional voting categorical data sets show that our method can find the optimal global solutions and can make up the K-Modes shortcoming.

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Section: B Data Setsmentioning

confidence: 99%

“…In [8] authors presented the genetic fuzzy k-Modes algorithm for clustering categorical data sets. They treated the fuzzy k-Modes clustering as an optimization problem and used GA to solve the problem in order to obtain globally optimal solution.…”

Section: Related Workmentioning

confidence: 99%

Clustering categorical data using a swarm-based method

Izakian

Abraham

Snášel

2009

2009 World Congress on Nature &Amp; Biologically Inspired Computing (NaBIC)

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“…selection, crossover and mutation are applied to current population to obtain new population. A genetic Algorithm is combined with fuzzy k-modes clustering algorithm is proposed in [11] to obtain the global optimum solution.…”

Section: Related Workmentioning

confidence: 99%

CLUSTERING CATEGORICAL DATA USING k-MODES BASED ON CUCKOO SEARCH OPTIMIZATION ALGORITHM

Lakshmi¹,

Visalakshi²,

Shanthi³

et al. 2017

IJSC

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Cluster analysis is the unsupervised learning technique that finds the interesting patterns in the data objects without knowing class labels. Most of the real world dataset consists of categorical data. For example, social media analysis may have the categorical data like the gender as male or female. The k-modes clustering algorithm is the most widely used to group the categorical data, because it is easy to implement and efficient to handle the large amount of data. However, due to its random selection of initial centroids, it provides the local optimum solution. There are number of optimization algorithms are developed to obtain global optimum solution. Cuckoo Search algorithm is the population based metaheuristic optimization algorithms to provide the global optimum solution. Methods: In this paper, k-modes clustering algorithm is combined with Cuckoo Search algorithm to obtain the global optimum solution. Results: Experiments are conducted with benchmark datasets and the results are compared with k-modes and Particle Swarm Optimization with k-modes to prove the efficiency of the proposed algorithm.

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“…However, FCM is an effective algorithm; the random selection in center points makes iterative process falling into the local optimal solution easily. To tackle this problem, evolutionary algorithms such as genetic algorithm (GA), differential evolution (DE), ant colony optimization (ACO), and particle swarm optimization (PSO) have been successfully applied [11,12,13,14].…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Clustering Approach using Artificial Bee Colony (ABC) and Particle Swarm Optimization

Karthikeyan¹,

Christopher²

2014

IJCA

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In this paper, Cluster analysis is a group objects like observations, events etc based on the information that are found in the data describing the objects or their relations. The main goal of the clustering is that the objects in a group will be similar or related to one other and different from (or unrelated to) the objects in other groups. In this paper, proposed a hybrid model of PSABC algorithm. The PSABC algorithm is a combination of Particle Swarm Algorithm (PSO) and Artificial Bee Colony (ABC) Algorithm used for data clustering on benchmark problems.The PSABC algorithm is compared with other existing classification techniques to evaluate the performance of the proposed approach. Thirteen of typical test data sets from the UCI Machine Learning Repository are used to demonstrate the results of the techniques. The simulation results indicate that PSABC algorithm can efficiently be used for multivariate data clustering.

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A genetic fuzzy k-Modes algorithm for clustering categorical data

Cited by 90 publications

References 14 publications

Clustering categorical data using a swarm-based method

Clustering categorical data using a swarm-based method

CLUSTERING CATEGORICAL DATA USING k-MODES BASED ON CUCKOO SEARCH OPTIMIZATION ALGORITHM

A Hybrid Clustering Approach using Artificial Bee Colony (ABC) and Particle Swarm Optimization

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