Comprehensive Study and Analysis of Partitional Data Clustering Techniques

Data clustering has found significant applications in various domains like bioinformatics, medical data, imaging, marketing study and crime analysis. There are several types of data clustering such as partitional, hierarchical, spectral, density-based, mixture-modeling to name a few. Among these, partitional clustering is well suited for most of the applications due to the less computational requirement. An analysis of various literatures available on partitional clustering will not only provide good knowledge, but will also lead to find the recent problems in partitional clustering domain. Accordingly, it is planned to do a comprehensive study with the literature of partitional data clustering techniques. In this paper, thirty three research articles have been taken for survey from the standard publishers from 2005 to 2013 under two different aspects namely the technical aspect and the application aspect. The technical aspect is further classified based on partitional clustering, constraint-based partitional clustering and evolutionary programming-based clustering techniques. Furthermore, an analysis is carried out, to find out the importance of the different approaches that can be adopted, so that any new development in partitional data clustering can be made easier to be carried out by researchers.

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Clustering Techniques and Their Applications: A Review

American Journal of Advanced Computing ◽

10.15864/ajac.1404 ◽

2020 ◽

Vol 1 (4) ◽

pp. 1-6

Author(s):

Arjun Dutta

Keyword(s):

Data Mining ◽

Image Segmentation ◽

Unsupervised Learning ◽

Clustering Analysis ◽

Data Clustering ◽

Clustering Method ◽

Clustering Techniques ◽

Partitional Clustering ◽

Gene Information ◽

Scientific Fields

This paper deals with concise study on clustering: existing methods and developments made at various times. Clustering is defined as an unsupervised learning where the targets are sorted out on the foundation of some similarity inherent among them. In the recent times, we dispense with large masses of data including images, video, social text, DNA, gene information, etc. Data clustering analysis has come out as an efficient technique to accurately achieve the task of categorizing information into sensible groups. Clustering has a deep association with researches in several scientific fields. k-means algorithm was suggested in 1957. K-mean is the most popular partitional clustering method till date. In many commercial and non-commercial fields, clustering techniques are used. The applications of clustering in some areas like image segmentation, object and role recognition and data mining are highlighted. In this paper, we have presented a brief description of the surviving types of clustering approaches followed by a survey of the areas.

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Categorization of Data Clustering Techniques

Handbook of Research on Public Information Technology ◽

10.4018/978-1-59904-857-4.ch052 ◽

2008 ◽

pp. 568-577

Author(s):

Baoying Wang ◽

Imad Rahal ◽

Richard Leipold

Keyword(s):

Unsupervised Learning ◽

Supervised Learning ◽

Data Clustering ◽

Analysis Data ◽

Discovery Process ◽

Data Set ◽

Market Basket ◽

Clustering Techniques ◽

Data Points ◽

Class Labels

Data clustering is a discovery process that partitions a data set into groups (clusters) such that data points within the same group have high similarity while being very dissimilar to points in other groups (Han & Kamber, 2001). The ultimate goal of data clustering is to discover natural groupings in a set of patterns, points, or objects without prior knowledge of any class labels. In fact, in the machine-learning literature, data clustering is typically regarded as a form of unsupervised learning as opposed to supervised learning. In unsupervised learning or clustering, there is no training function as in supervised learning. There are many applications for data clustering including, but not limited to, pattern recognition, data analysis, data compression, image processing, understanding genomic data, and market-basket research.

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Big Data Clustering Techniques: Recent Advances and Survey

Machine Learning and Data Mining for Emerging Trend in Cyber Dynamics ◽

10.1007/978-3-030-66288-2_3 ◽

2021 ◽

pp. 57-79

Author(s):

Hassan Ibrahim Hayatu ◽

Abdullahi Mohammed ◽

Ahmad Barroon Isma’eel

Keyword(s):

Big Data ◽

Data Clustering ◽

Clustering Techniques ◽

Recent Advances

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Wind turbine power output very short-term forecast: A comparative study of data clustering techniques in a PSO-ANFIS model

Journal of Cleaner Production ◽

10.1016/j.jclepro.2020.120135 ◽

2020 ◽

Vol 254 ◽

pp. 120135 ◽

Cited By ~ 15

Author(s):

Paul A. Adedeji ◽

Stephen Akinlabi ◽

Nkosinathi Madushele ◽

Obafemi O. Olatunji

Keyword(s):

Comparative Study ◽

Wind Turbine ◽

Power Output ◽

Data Clustering ◽

Short Term ◽

Anfis Model ◽

Clustering Techniques ◽

Short Term Forecast ◽

Term Forecast

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EKEGWO: Enhanced Kernel-Based Exponential Grey Wolf Optimizer for Bi-Objective Data Clustering

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems ◽

10.1142/s0218488519500296 ◽

2019 ◽

Vol 27 (04) ◽

pp. 669-688 ◽

Cited By ~ 1

Author(s):

Amolkumar Narayan Jadhav ◽

Gomathi N.

Keyword(s):

Data Clustering ◽

Clustering Algorithm ◽

Fitness Function ◽

Multidimensional Data ◽

Grey Wolf Optimizer ◽

Grey Wolf ◽

Widespread Application ◽

Clustering Techniques ◽

Cluster Distance ◽

F Measure

The widespread application of clustering in various fields leads to the discovery of different clustering techniques in order to partition multidimensional data into separable clusters. Although there are various clustering approaches used in literature, optimized clustering techniques with multi-objective consideration are rare. This paper proposes a novel data clustering algorithm, Enhanced Kernel-based Exponential Grey Wolf Optimization (EKEGWO), handling two objectives. EKEGWO, which is the extension of KEGWO, adopts weight exponential functions to improve the searching process of clustering. Moreover, the fitness function of the algorithm includes intra-cluster distance and the inter-cluster distance as an objective to provide an optimum selection of cluster centroids. The performance of the proposed technique is evaluated by comparing with the existing approaches PSC, mPSC, GWO, and EGWO for two datasets: banknote authentication and iris. Four metrics, Mean Square Error (MSE), F-measure, rand and jaccord coefficient, estimates the clustering efficiency of the algorithm. The proposed EKEGWO algorithm can attain an MSE of 837, F-measure of 0.9657, rand coefficient of 0.8472, jaccord coefficient of 0.7812, for the banknote dataset.

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