Data Mining Process Using Clustering: A Survey

Clustering is a process of dividing the data into groups of similar objects and dissimilar ones from other objects. Representation of data by fewer clusters necessarily loses fine details, but achieves simplification. Data is model by its clusters. Clustering plays an significant part in applications of data mining such as scientific data exploration, information retrieval, text mining, city-planning, earthquake studies, marketing, spatial database applications, Web analysis, marketing, medical diagnostics, computational biology, etc. Clustering plays a role of active research in several fields such as statistics, pattern recognition and machine learning. Data mining adds complications to very large datasets with many attributes of different types to clustering. Unique computational requirements are imposed on relevant clustering algorithms. A variety of clustering algorithms have recently emerged that meet the various requirements and were successfully applied to many real-life data mining problems.

Clustering is a technique of grouping similar data objects in one group and dissimilar data objects in other group. 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. Grouping of data into clusters aims to maximize the intra class similarity and also minimize the inter class similarity. The clustering techniques can be categorized into partitioning methods, hierarchical methods, density-based methods and grid-based methods. This paper aims to provide a brief overview and complexities of various clustering algorithms.

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, ...

Clustering is a process of keeping similar data into groups.Objects within the cluster/group have high similarity in comparison to one another but are very dissimilar to objects of other clusters. Clustering is an unsupervised learning technique as every other problem of this kind; it deals with finding a structure in a collection of unlabeled data. Types of clustering methods are–hierarchical and partitioning based. In this paper clustering and its methods are discussed.

2015

The main aim of this review paper is to provide a comprehensive review of different clustering techniques in data mining. Clustering is the subject of active research in many fields such as statistics, pattern recognition and machine learning. Cluster Analysis is an excellent data mining tool for a large and multivariate database. Clustering is the one of data mining techniques in which data is divided into the groups of similar objects Clustering is a suitable example of unsupervised classification. Unsupervised means that clustering does not depends on pre defined classes and training examples during classifying the data objects. Classification refers to assigning data objects to a set of classes.

— The Data Mining process is used to extract valuable information from large & different categories of data set. Extraction is transformation of information from data set into an understandable structure for further use. Data Mining & Data Analysis applications work on most important concept of Clustering. In clustering data is divided into groups of similar objects. Data is represented by fewer clusters which necessarily involves certain fine details, but achieves simplification. In modern research Clustering Algorithms are vital tools for data analytics. The Clustering algorithms have been applied in variety of fields like neural networks, economics, Image Processing, biology etc. Most challenging problem in clustering is unsupervised grouping of patterns. This paper aims to provide survey of Clustering Algorithms.

Cluster analysis is the duty of assemblage a set of items in such a manner that items in the same group are more alike to each other than to those in other groups .A collection of data entities can be treated as one group. Whereas undertaking gathering investigation, we first distinct the regular of records into groups based on data association and then assign the tags to the groups. The main advantage of gathering over arrangement is that, it is adaptable to variations and helps single out useful features that distinguishes dissimilar groups. It is a most significant tasks to efficient the data mining, and a common method for numerical data analysis, used in numerous fields. In This paper converse about different types of clustering algorithms such as Partitioning Method, Hierarchical Method Density-based Method, Grid-Based Method, Model-Based Method, and Constraint-based Method.

2017 6th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), 2017

the foremost illustrative task in data mining process is clustering. It plays an exceedingly important role in the entire KDD process also as categorizing data is one of the most rudimentary steps in knowledge discovery. It is an unsupervised learning task used for exploratory data analysis to find some unrevealed patterns which are present in data but cannot be categorized clearly. Sets of data can be designated or grouped together based on some common characteristics and termed clusters , the mechanism involved in cluster analysis are essentially dependent upon the primary task of keeping objects with in a cluster more closer than objects belonging to other groups or clusters. Depending on the data and expected cluster characteristics there are different types of clustering paradigms. In the very recent times many new algorithms have emerged which aim towards bridging the different approaches towards clustering and merging different clustering algorithms given the requirement of handling sequential ,extensive data with multiple relationships in many applications across a broad spectrum. Various clustering algorithms have been developed under different paradigms for grouping scattered data points and forming efficient cluster shapes with minimal outliers. This paper attempts to address the problem of creating evenly shaped clusters in detail and aims to study, review and analyze few clustering algorithms falling under different categories of clustering paradigms and presents a detailed comparison of their efficiency, advantages and disadvantages on some common grounds. This study also contributes in correlating some very important characteristics of an efficient clustering algorithm.

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.

Data mining involves working with very huge amount of data. This data always contains some natural groups of data points wherein the data points within these groups (clusters) are identical to each other. Finding these set of clusters is an unsupervised approach and various clustering methods exists to accomplish this task. Density based, partitioning based, hierarchical and grid based are some of the common clustering methods which groups the similar data in the clusters with common goal of increasing the intra cluster similarity and decreasing the inter cluster similarity. Each of these methods contains several algorithms which are briefly discussed in this review paper.