A Survey of Partition based Clustering Algorithms in Data Mining: An Experimental Approach

Velmurugan, T.; Santhanam, T.

doi:10.3923/itj.2011.478.484

Cited by 73 publications

(40 citation statements)

References 4 publications

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“…Considering the final outcome to take in another protest or comprehend another marvel, peoples dependably attempt to look for the features that can portray it, and further contrast it and other known items or wonders, in view of the similitude or uniqueness, summed up as nearness, as per some specific measures or standards. "Fundamentally, classification frameworks are either supervised or unsupervised, contingent upon whether they relegate new contributions to one of a limited number of discrete regulated classes or unsupervised classifications [3].…”

Section: Introductionmentioning

confidence: 99%

A Complete Review and Comparative Study on Analysis of Data Clusters in Mining

Vinodani¹,

Jamshed²,

Kumar³

2018

IJCA

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Clustering is a segment of data science into clusters of same items. Showing the data by less clusters essentially loses many fine points of interest, however accomplishes rearrangements. It shows data by the clusters. Data modeling places clustering in a verifiable pattern in data science, estimation, and numerical examination. As the machine learning is being considered so as to compare the clusters shrouded designs, the look of clusters is just like unsupervised learning, and the methodology says about the data idea. As the reasoning of the clusters is being considered which is taken as exception in the data mining, for eg, logical data analysis, data recovery and content mining, spatial database applications, Web examination, CRM, promoting, medicinal diagnostics, computational science, and numerous others. Clustering of data is being taken as the dynamic segment of many fields as estimation, design and artificial intelligence, which actually considers the clustering in the information science. Various type of the properties of the data points are considered for clustering the data points into the clusters. Some very meaningful algorithms are being used in the various clustering methodologies. The work in the paper provides the quick review of many clustering methodologies which works on various properties defined by the data points in the dataset.

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Section: Introductionmentioning

confidence: 99%

A Complete Review and Comparative Study on Analysis of Data Clusters in Mining

Vinodani¹,

Jamshed²,

Kumar³

2018

IJCA

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“…The k-means algorithm is the most widely used algorithm for data mining applications [6,27]. It is simple, scalable, easily understood, and can be adopted to work with high-dimensional data [28][29][30].…”

Section: Related Workmentioning

confidence: 99%

An Anomaly Detection Based on Optimization

Alguliyev¹,

Alıguliyev²,

İmamverdiyev³

et al. 2017

IJISA

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Abstract-At present, an anomaly detection is one of the important problems in many fields. The rapid growth of data volumes requires the availability of a tool for data processing and analysis of a wide variety of data types. The methods for anomaly detection are designed to detect object's deviations from normal behavior. However, it is difficult to select one tool for all types of anomalies due to the increasing computational complexity and the nature of the data. In this paper, an improved optimization approach for a previously known number of clusters, where a weight is assigned to each data point, is proposed. The aim of this article is to show that weighting of each data point improves the clustering solution. The experimental results on three datasets show that the proposed algorithm detects anomalies more accurately. It was compared to the k-means algorithm. The quality of the clustering result was estimated using clustering evaluation metrics. This research shows that the proposed method works better than k-means on the Australia (credit card applications) dataset according to the Purity, Mirkin and F-measure metrics, and on the heart diseases dataset according to F-measure and variation of information metric.

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“…Here, each partition is represented by either a centroid or a medoid. A centroid is an average of all data objects in a partition, while the medoid is the most representative point of a cluster [7]. The fundamental requirements of the partitioning based methods are each cluster must contain at least one data object, and each data objects must belong to exactly one cluster.…”

Section: Partitioning Based Clusteringmentioning

confidence: 99%

“…It imposes user's constraints on clustering such as user's requirement or explains properties of the required clustering results. [7]Among all these methods, this paper is aimed to explore two methods -K-means which is partitioning based clustering method and Hierarchal based clustering methods. We compare them by using some criterion function.…”

Section: Introductionmentioning

confidence: 99%

Time Improving Policy of Text Clustering Algorithm by Reducing Computational Overheads

Gupta¹,

Rajavat²

2015

IJCA

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Since the amount of text data stored in computer repositories is growing every day, we need more than ever a reliable way to assemble or classify text documents. Clustering can provide a means of introducing some form of organization to the data, which can also serve to highlight significant patterns and trends .Document clustering is used in many fields such as data mining and information retrieval. This paper presents the results of an experimental study of some common document clustering techniques. In particular, we compare the two main approaches of document clustering, agglomerative hierarchical clustering Modified BIRCH and Partitional clustering algorithm K-means. As a result of comparing both algorithms we attempt to establish appropriate clustering technique to generate qualitative clustering of real world document.

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A Survey of Partition based Clustering Algorithms in Data Mining: An Experimental Approach

Cited by 73 publications

References 4 publications

A Complete Review and Comparative Study on Analysis of Data Clusters in Mining

A Complete Review and Comparative Study on Analysis of Data Clusters in Mining

An Anomaly Detection Based on Optimization

Time Improving Policy of Text Clustering Algorithm by Reducing Computational Overheads

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