Clustering Techniques on Text Mining: A Review

The amount of digital data utilized in daily life has increased owing to the high dependence on such data. Most data can be stored in textual documents. With the rapid increase in the number of textual documents, users face problems in obtaining useful information. Thus, a method by which to manage data is required to give users an idea about content. In addition, techniques to increase the ratio of precision in information retrieval results are also needed. Therefore, the textual document clustering area is developed to represent the data in meaningful clusters. The two main factors encountered in the process of textual document clustering are efficiency and goodness or quality of data clusters. Efforts have been exerted to deal with these factors. These attempts can be categorized into either traditional or modern approaches. However, these attempts also face numerous issues. In this paper, we present the previous and current issues faced by textual document clustering algorithms to help text domain researchers understand these issues. This study provides researchers and students an overview about textual document clustering algorithms. Furthermore, this study can encourage researchers to find solutions to these issues.

2015

Data mining is the process of non-trivial discovery from implied, previously unknown, and potentially useful information from data in large databases. Hence it is a core element in knowledge discovery, often used synonymously. Clustering, one of technique for data mining used for grouping similar terms together. Earlier statistical analysis used in text mining depends on term frequency. Then, new concept based text mining model was introduced which analyses terms. Clustering of document is useful for the purpose of document organization, summarization, and information retrieval in an efficient way. Initially, clustering is applied for enhancing the information retrieval techniques. Of late, clustering techniques have been applied in the areas which involve browsing the gathered data or in categorizing the outcome provided by the search engines for the reply to the query raised by the users. In this paper, we are providing a comprehensive survey over the document clustering.

2015

Data mining , knowledge discovery is the process of analyzing data from different perspectives and summarizing it into useful information information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases. 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? It can be shown that there is no absolute “best” criterion which would be independent of the final aim of the clustering. Consequently, it is the user which must supply this criterion, in such a way that the result of the clustering will suit

2014

Knowledge discovery is a process of discovering useful knowledge from a collection of data. This widely used data mining technique is a process that includes data preparation and selection, data cleansing, incorporating prior knowledge on data sets and interpreting accurate solutions from the observed results. Text mining is a sub domain of knowledge discovery from the text data. The presented study provides a broad way of understanding the text mining and their applications in different domain of real time applications. The text mining includes the process of text classification and text clustering. On the other hand, the cluster analysis is performed on the un-labelled and unstructured data. In this paper, I have presented a study of various research papers that explore the area of Text Clustering approaches in various genres.

International Journal of Engineering Research and, 2016

With the huge upsurge in information, it has become difficult to gather relevant information within the limited time. Hence clustering methods are introduced to ease the task of gathering the relevant information in a cluster. Efficiency of clustering therefore becomes one of the crucial requirements to be met by the clustering methods. There are several methods and algorithms have been introduced. Hierarchical clustering is often portrayed as the better quality clustering approach, but it is limited because of its time complexity. In contrast, K-means and its variants have a time complexity which is linear in the number of documents. A clustering method based on the hidden semantics within the documents is proposed here for better results. The proposed method extracts features from the web documents using conditional random fields and builds a linguistic topological space based on the associations of features. The features that are used this method are TF (Term Frequency) and IDF (Inverse Document Frequency). Both TF and IDF values are best in reflecting the importance of the document in the given context. Then the documents are clustered based on the K-means clustering after finding the topics in the documents using these features. The advantage of K-means method is that it produces tighter clusters than hierarchical clustering, especially if the clusters are globular.

IJRCAR, 2014

Text mining is a technique to find meaningful patterns from the available text documents. The pattern discovery from the text and document organization of document is a well-known problem in data mining. Analysis of text content and categorization of the documents is a complex task of data mining. In order to find an efficient and effective technique for text categorization, various techniques of text categorization and classification is recently developed. Some of them are supervised and some of them unsupervised manner of document arrangement. This presented paper discusses different method of text categorization and cluster analysis or text documents. In addition of that a new text mining technique is proposed for future implementation.

Clustering is a widely studied data mining problem in the text domains. The problem finds numerous applications in customer segmentation, classification, collaborative filtering, visualization, document organization , and indexing. In this chapter, we will provide a detailed survey of the problem of text clustering. We will study the key challenges of the clustering problem, as it applies to the text domain. We will discuss the key methods used for text clustering, and their relative advantages. We will also discuss a number of recent advances in the area in the context of social network and linked data.

Text clustering is a text mining technique used to group text documents into groups (or clusters) based on similarity of content. This organization (i.e. clustering) is so as to make documents more understandable and easier to search the relevant information, easier to process, and even more efficient in utilizing communication bandwidth and storage space. An example is clustering results of a web search engine operation into groups of similar documents. Many text clustering algorithms have been developed using different approaches, but none can be said to be the best. The choice of a particular algorithm is a big issue to text clustering system developers. K Means is arguably the most popular text clustering algorithm. However, just like the others, it must be having its own weaknesses. In this paper, we explore the K Means algorithm as well as its variants and discuss their appropriateness in text clustering. We describe the characteristics of the algorithms accompanied by some examples and illustrations in an attempt to discover the strengths and weaknesses. The paper thus gives an in depth view of the K Means algorithms, discusses the appropriateness of the algorithms, and also gives guidance to researchers of text mining concerning the choice of K Means for text clustering.

2019

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2021

Clustering is a widely used unsupervised data mining technique. In clustering, the main aim is to put similar data objects in one cluster and dissimilar in another cluster. The k-implies is the most famous clustering algorithm because of its effortlessness. But the performance of the k-means clustering algorithm depends upon the parameter selection. Parameter selection like number of cluster and initial cluster center are key of k-means algorithm. Distance augmentation method, density method quadratic clustering methods are utilized to initial cluster selection. This paper examination five unique methods, for example, improved k-means text clustering algorithm, revisiting k-means, LMMK algorithm, SELF-DATA architecture, Clustering Approach for Relation e.t.c. But these techniques have some limitations. To improve these approach, this paper has proposed the development of text clustering method with k-means for analysis of text data.