COMPARATIVE STUDY OF VARIOUS CLUSTERING TECHNIQUES

This paper presents a review on various clustering techniques used in data mining. Data mining is the task of retrieving useful and hidden knowledge from data sets [1] [2]. Clustering is one of the important tasks of data mining. Clustering is an unsupervised learning problem which is used to determine the intrinsic grouping in a set of unlabeled data [3]. The grouping of objects is done on the principle of maximizing the intra-cluster similarity and minimizing the inter-cluster similarity in such a way that the objects in the same group/cluster share some similar properties/traits [4].

Data mining is an integrated field, depicted technologies in combination to the areas having database, learning by machine, statistical study, and recognition in patterns of same type, information regeneration, A.I networks, knowledge-based portfolios, artificial intelligence, neural network, and data determination. In real terms, mining of data is the investigation of provisional data sets for finding hidden connections and to gather the information in peculiar form which are justifiable and understandable to the owner of gather or mined data. An unsupervised formula which differentiate data components into collections by which the components in similar group are more allied to one other and items in rest of cluster seems to be non-allied, by the criteria of measurement of equality or predictability is called process of clustering. Cluster analysis is a relegating task that is utilized to identify same group of object and it is additionally one of the most widely used method for many practical application in data mining. It is a method of grouping objects, where objects can be physical, such as a student or may be a summary such as customer comportment, handwriting. It has been proposed many clustering algorithms that it falls into the different clustering methods. The intention of this paper is to provide a relegation of some prominent clustering algorithms.

A study will be made on the various clustering methods. Method of grouping set of physical or abstract objects into classes of similar objects is called as clustering. Splitting a data set into groups such that the similarity within a group is larger than among groups are done by clustering algorithm. This paper discusses about few types of clustering methods-Partitioning methods, Hierarchical methods, Density-based methods, Grid-based method, Model based methods, Constraint based methods.

Clustering is a division of data into groups of similar objects. Representing the data by fewer clusters necessarily loses certain fine details, but achieves simplification. It models data by its clusters. Data modeling puts clustering in a historical perspective rooted in mathematics, statistics, and numerical analysis. From a machine learning perspective clusters correspond to hidden patterns, the search for clusters is unsupervised learning, and the resulting system represents a data concept. From a practical perspective clustering plays an outstanding role in data mining applications such as scientific data exploration, information retrieval and text mining, spatial database applications, Web analysis, CRM, marketing, medical diagnostics, computational biology, and many others.

IJCSMC, 2018

Data mining is the search or the discovery of new information in the form of patterns from huge sets of data. The goal of data mining is to provide companies with valuable, hidden insights which are present in their large databases. Clustering is one such important technique that is used in data mining. It is the process of grouping similar data items into clusters and hence we can define a cluster as a group of items which are similar between them and dissimilar between items of another cluster. This paper is intended to examine and evaluate various data clustering methods and algorithms. The purpose of discussing the various algorithms is to make beginners and new researchers understand the working of clustering methods, which will help them to come up with new approaches for the improvement of these methods.

2016

The goal of the data mining process is to extract information from a large data set and transform it into an different usable form for further use. Clustering is very important in data analysis and in different data mining applications. Clustering is a division of data into groups of similar objects. Each group, called cluster, consists of objects that are similar between themselves and dissimilar to objects of other groups. Clustering deals with finding a structure in a collection of unlabeled data. There are different clustering algorithms are used to organize data, categorize data, for data compression and model construction etc. This paper analyzes the four major clustering algorithms namely: Partitioning methods, Hierarchical methods, Grid-based methods and Density-based methods and comparing the performance of these algorithms on the basis of correctly class wise cluster building ability of algorithm. Keywords—Data mining, Clustering, Hierarchical method, Partitioning method, ...

The main aim of Data mining process is to discover meaningful trends and patterns from the data hidden in repositories. For data analysis and data mining application, Clustering is important. It is a process or technique of grouping a set of objects that belong to the same class. Cluster analysis or Clustering has been widely used in several disciplines, such as statistics, software engineering, biology, psychology and other social sciences, in order to identify natural groups in large amounts of data. These data sets are constantly becoming larger, and their dimensionality prevents easy analysis and validation of the results. There are various clustering techniques like Simple K-Means, EM, Farthest First, Filtered Clustering, Hierarchical Clustering etc. In this research work, a brief introduction to cluster analysis is given. I. Introduction Data mining is the process of extracting interesting information from large amount of data stored in different databases or data warehouses. Data mining tools can be used to predict future in the field of business, knowledge driven systems. The data collection and management systems are already available in mid-range companies but the challenge is to convert this data into success. The actual data mining task is the automatic or semi-automatic analysis of large quantities of data to extract previously unknown interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection) and dependencies (association rule mining). Several Data mining techniques are present like classification, association, clustering, etc. In this research paper clustering analysis is discussed. Clustering means identifying and making groups. A good clustering algorithm is able to identity clusters irrespective of their shapes. Cluster analysis itself is not one specific algorithm, but it can be achieved by several algorithms. Let's take some examples, in city planning; clustering technique helps in identifying groups of houses according to their house type, value and geographical location, in marketing, clustering technique help marketers discover distinct groups in their customer bases, and then use this knowledge to develop targeted marketing programs.

Grouping Multidimensional Data

Clustering is a division of data into groups of similar objects. Representing the data by fewer clusters necessarily loses certain fine details, but achieves simplification. It models data by its clusters. Data modeling puts clustering in a historical perspective rooted in mathematics, statistics, and numerical analysis. From a machine learning perspective clusters correspond to hidden patterns, the search for clusters is unsupervised learning, and the resulting system represents a data concept. From a practical perspective clustering plays an outstanding role in data mining applications such as scientific data exploration, information retrieval and text mining, spatial database applications, Web analysis, CRM, marketing, medical diagnostics, computational biology, and many others. Clustering is the subject of active research in several fields such as statistics, pattern recognition, and machine learning. This survey focuses on clustering in data mining. Data mining adds to clustering the complications of very large datasets with very many attributes of different types. This imposes unique computational requirements on relevant clustering algorithms. A variety of algorithms have recently emerged that meet these requirements and were successfully applied to real-life data mining problems. They are subject of the survey.

Through data mining, we can able to effectively extract data in the form of knowledge discovery which provides useful helping guide for information processing that can be utilized in varieties of applications. It is the most sought after field in recent scenario and its importance cannot be ignored at all as effective data analysis outputs to extensive information utilization in almost all the fields and a proper data mining provides the appropriate and effective result. In this paper we focus on basics of clustering techniques and different major clustering methods.

IOSR Journal of Engineering, 2012

Clustering is a common technique for statistical data analysis, which is used in many fields, including machine learning, data mining, pattern recognition, image analysis and bioinformatics. Clustering is the process of grouping similar objects into different groups, or more precisely, the partitioning of a data set into subsets, so that the data in each subset according to some defined distance measure. This paper covers about clustering algorithms, benefits and its applications. Paper concludes by discussing some limitations.