Study on Various Clustering Techniques

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.

The purpose of the data mining technique is to mine information from a bulky data set and make it into a reasonable form for supplementary purpose. Data mining can do by passing through various phases. Mining can be done by using supervised and unsupervised learning. Clustering is a significant task in data analysis and data mining applications. It is the task of arranging a set of objects so that objects in the identical group are more related to each other than to those in other groups (clusters). The clustering is unsupervised learning. Clustering algorithms can be classified into partition-based algorithms, hierarchical-based algorithms, density-based algorithms and grid-based algorithms. This paper focuses on a keen study of different clustering algorithms in data mining. A brief overview of various clustering algorithms is discussed.

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.

IJCSMC, 2018

Data mining refers to the process of extracting information from a large amount of data and transforming it into an understandable form. Clustering is one of the most important methodology in the field of data mining. It is an unsupervised machine learning technique. Clustering means grouping a set of objects so that similar objects present in the same group and dissimilar objects present in different groups. This paper provides a broad survey on various clustering techniques and also analyzes the advantages and shortcomings of each technique.

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.

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, ...

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.

2012

Partitioning a set of objects into homogeneous clusters is a fundamental operation in data mining. The operation is needed in a number of data mining tasks. Clustering or data grouping is the key technique of the data mining. It is an unsupervised learning task where one seeks to identify a finite set of categories termed clusters to describe the data . The grouping of data into clusters is based on the principle of maximizing the intra class similarity and minimizing the inter class similarity. The goal of clustering is to determine the intrinsic grouping in a set of unlabeled data. But how to decide what constitutes a good clustering? This paper deal with the study of various clustering algorithms of data mining and it focus on the clustering basics, requirement, classification, problem and application area of the clustering algorithms.

2013

Retrieval of information from the databases is now a day's significant issues. The thrust of information for decision making is challenging one. To overcome this problem, different techniques have been developed for this purpose. One of techniques is clustering. Clustering is a significant task in data analysis and data mining applications. It is the task of arrangement a set of objects so that objects in the identical group are more related to each other than to those in other groups (clusters).The clustering is unsupervised learning. In this paper we propose a methodology for comparing clustering methods based on the quality of the result and the performance of the execution. The quality of a clustering result depends on both the similarity measure used by the method and its implementation. Clustering has been widely used as a segmentation approach therefore, choosing an appropriate clustering method is very critical to achieve better results. A good clustering method will produce high superiority clusters with high intra-class similarity and low inter-class similarity. There are different types of Clustering algorithms partition-based algorithms such as K-Means, KNN, density-based algorithms and SSC-EA-based algorithms. Partitioning clustering algorithm splits the data points into k partition, where each partition represents a cluster. Density based algorithms find the cluster according to the regions which grow with high density. It is the onescan algorithms.

The International journal of analytical and experimental modal analysis, 2014

Clustering is the assignment of data objects (records) into groups (called clusters) so that data objects from the same cluster are more similar to each other than objects from different clusters. Clustering techniques have been discussed extensively in similarity search, Segmentation, Statistics, Machine Learning ,Trend Analysis, Pattern Recognition and classification. Clustering methods can be classified in to i)partition methods2)Hierarchical methods,3)Density Based methods 4)Grid based methods5)Model Based methods. in this paper, i would like to give review about clustering methods by taking some example for each classification. I am also providing comparative statement by taking constraints i.e Data type, Cluster Shape, Complexity, Data Set, Measure, Advantages and Disadvantages.