Exploration of Various Clustering Algorithms for Text Mining

Garg, Neha; Gupta, Rajat

doi:10.5815/ijeme.2018.04.02

Cited by 5 publications

(4 citation statements)

References 8 publications

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“…Clustering models can group the data where data might be students, questions, comments, etc. ; these models are helpful to identify the similarities, main themes, patterns, or trends (He, 2013;Dringus & Ellis, 2004;Dhillon & Modha, 2001;Garg & Gupta, 2018). Visualization techniques such as word clouds use word frequencies in the document to discover themes in the data (Brooks, Gilbuena, Krause, & Koretsky, 2014).…”

Section: Text Mining In Education Researchmentioning

confidence: 99%

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Developing Statistical Understanding and Overcoming Anxiety via Drop-in Consultations

İntepe

Shearman

2020

SERJ

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Students in many Australian universities start their studies mathematically underprepared as there are no prerequisites for mathematics, and assumed knowledge requirements are often overlooked. Many degrees include at least one statistics subject for which students require a reasonable level of mathematical ability to successfully complete. Students’ efforts to grasp quantitative skills often lead to feelings of anxiety, stress, and lack of self-confidence. The Mathematics Education Support Hub (MESH) at WSU provides free support in mathematics and statistics to all students, both undergraduate and postgraduate, to increase their engagement, understanding, and abilities in statistics as well as to overcome their anxiety. In this paper we focus on the drop-in consultation service, which provides “just in time” help in campus libraries. Data is collected for every consultation, which enabling an investigation in relation to the mathematics background of students and the problematic topics in statistics. Text mining is used to examine students’ queries to identify the topics in statistics subjects that students struggle most with. Outcomes of this analysis can be used by statistics instructors and mathematics support centres to improve students’ experience in learning and to help to reduce statistics anxiety in future generations of statistics students. First published February 2020 at Statistics Education Research Journal Archives

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Section: Text Mining In Education Researchmentioning

confidence: 99%

“…In our case we are clustering terms, which are the columns of the document-by-term matrix, regarding their appearance in the queries. The similarity between these column vectors is determined by a distance metric; we used the cosine metric (Garg & Gupta, 2018;Gupta & Lehal, 2009).…”

Section: Analysis Of the Textmentioning

confidence: 99%

Developing Statistical Understanding and Overcoming Anxiety via Drop-in Consultations

İntepe

Shearman

2020

SERJ

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“…Garga and Guptab [10] studied various partitioning and hierarchical clustering algorithms used in text mining alongside their metrics and demerits in detail. Moreover, they have discussed the ideas of proficiently using the algorithms for effective clustering of text documents.…”

Section: Related Workmentioning

confidence: 99%

An Extensive Study of Similarity and Dissimilarity Measures Used for Text Document Clustering using K-means Algorithm

Afzali¹,

Kumar²

2018

IJITCS

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In today's world tremendous amount of unstructured data, especially text, is being generated through various sources. This massive amount of data has lead the researchers to focus on employing data mining techniques to analyse and cluster them for an efficient browsing and searching mechanisms. The clustering methods like k-means algorithm perform through measuring the relationship between the data objects. Accurate clustering is based on the similarity or dissimilarity measure that is defined to evaluate the homogeneity of the documents. A variety of measures have been proposed up to this date. However, all of them are not suitable to be used in the k-means algorithm. In this paper, an extensive study is done to compare and analyse the performance of eight well-known similarity and dissimilarity measures that are applicable to the kmeans clustering approach. For experiment purpose, four text document data sets are used and the results are reported.

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“…In Stemming, morphologically similar words are clustered together under the hypothesis that they are semantically similar [2]- [7]. It is useful for Text Mining, Natural Language Processing (NLP) functions, Text clustering, Text categorization, Text summarization, and application of Text Mining (TM) [1], [8,9].…”

Section: Introductionmentioning

confidence: 99%

Influence of GUJarati STEmmeR in Supervised Learning of Web Page Categorization

Patel¹,

Patel²

2021

IJISA

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With the large quantity of information offered on-line, it's equally essential to retrieve correct information for a user query. A large amount of data is available in digital form in multiple languages. The various approaches want to increase the effectiveness of on-line information retrieval but the standard approach tries to retrieve information for a user query is to go looking at the documents within the corpus as a word by word for the given query. This approach is incredibly time intensive and it's going to miss several connected documents that are equally important. So, to avoid these issues, stemming has been extensively utilized in numerous Information Retrieval Systems (IRS) to extend the retrieval accuracy of all languages. These papers go through the problem of stemming with Web Page Categorization on Gujarati language which basically derived the stem words using GUJSTER algorithms [1]. The GUJSTER algorithm is based on morphological rules which is used to derived root or stem word from inflected words of the same class. In particular, we consider the influence of extracted a stem or root word, to check the integrity of the web page classification using supervised machine learning algorithms. This research work is intended to focus on the analysis of Web Page Categorization (WPC) of Gujarati language and concentrate on a research problem to do verify the influence of a stemming algorithm in a WPC application for the Gujarati language with improved accuracy between from 63% to 98% through Machine Learning supervised models with standard ratio 80% as training and 20% as testing.

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Exploration of Various Clustering Algorithms for Text Mining

Cited by 5 publications

References 8 publications

Developing Statistical Understanding and Overcoming Anxiety via Drop-in Consultations

Developing Statistical Understanding and Overcoming Anxiety via Drop-in Consultations

An Extensive Study of Similarity and Dissimilarity Measures Used for Text Document Clustering using K-means Algorithm

Influence of GUJarati STEmmeR in Supervised Learning of Web Page Categorization

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