Document Classification Using Expectation Maximization with Semi Supervised Learning

Bhawna Nigam1, Poorvi Ahirwal2, Sonal Salve3, Swati Vamney4, Department of Information Technology, IET, DAVV 

Abstract

As the amount of online document increases, the demand for document classification to aid the analysis and management of document is increasing. Text is cheap, but information, in the form of knowing what classes a document belongs to, is expensive. The main purpose of this paper is to explain the expectation maximization technique of data mining to classify the document and to learn how to improve the accuracy while using semi-supervised approach. Expectation maximization algorithm is applied with both supervised and semi-supervised approach. It is found that semi-supervised approach is more accurate and effective. The main advantage of semi supervised approach is “DYNAMICALLY GENERATION OF NEW CLASS”. The algorithm first trains a classifier using the labeled document and probabilistically classifies the unlabeled documents. The car dataset for the evaluation purpose is collected from UCI repository dataset in which some changes have been done from our side. 

Keywords 

Data mining, semi-supervised learning, supervised learning, expectation maximization, document classification. 

Original Source URL: https://airccse.org/journal/ijsc/papers/2411ijsc04.pdf

https://airccse.org/journal/ijsc/current2011.html

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2nd International Conference on NLP, Data Mining and Machine Learning (NLDML 2023)

January 21 ~ 22, 2023, Virtual Conference

Webpage URL: https://nldml2023.org/

Paper Submission

Authors are invited to submit papers through the conference Submission System by November 26, 2022. Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this conference. The proceedings of the conference will be published by International Journal on Cybernetics & Informatics (IJCI) (Confirmed).

Important Dates

• Submission Deadline: November 26, 2022

• Authors Notification: December 26, 2022

• Registration & Camera - Ready Paper Due: January 01, 2023

Contact us:

Here's where you can reach us: nldml@nldml2023.org (or) nldmlconf@yahoo.com

Submission URL:

https://nldml2023.org/submission/index.php

Evolving Efficient Clustering and Classification Patterns in Lymphography Data through Data Mining Techniques

Shomona Gracia Jacob1 and R.Geetha Ramani2, 1Rajalakshmi Engineering College, India, 2Anna University, Chennai, India

ABSTRACT

Data mining refers to the process of retrieving knowledge by discovering novel and relative patterns from large datasets. Clustering and Classification are two distinct phases in data mining that work to provide an established, proven structure from a voluminous collection of facts. A dominant area of modern-day research in the field of medical investigations includes disease prediction and malady categorization. In this paper, our focus is to analyze clusters of patient records obtained via unsupervised clustering techniques and compare the performance of classification algorithms on the clinical data. Feature selection is a supervised method that attempts to select a subset of the predictor features based on the information gain. The Lymphography dataset comprises of 18 predictor attributes and 148 instances with the class label having four distinct values. This paper highlights the accuracy of eight clustering algorithms in detecting clusters of patient records and predictor attributes and highlights the performance of sixteen classification algorithms on the Lymphography dataset that enables the classifier to accurately perform multi-class categorization of medical data. Our work asserts the fact that the Random Tree algorithm and the Quinlan’s C4.5 algorithm give 100 percent classification accuracy with all the predictor features and also with the feature subset selected by the Fisher Filtering feature selection algorithm.. It is also stated here that the Density Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm offers increased clustering accuracy in less computation time.

KEYWORDS

Data mining, Clustering, Feature Selection, Classification, Lymphography Data 

Original Source URL: https://airccse.org/journal/ijsc/papers/3312ijsc09.pdf

https://airccse.org/journal/ijsc/current2012.html

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Contact Us: ijscjournal@yahoo.com or ijsc@aircconline.com

Submission System: http://coneco2009.com/submissions/imagination/home.html

An Efficient Method for Assessing Water Quality Based on Bayesian Belief Networks

Khalil Shihab1 and Nida Al-Chalabi2

1College of Engineering & Science, Victoria University, Australia

2Department of Computer Science, SQU, Oman

ABSTRACT

A new methodology is developed to analyse existing water quality monitoring networks. This methodology incorporates different aspects of monitoring, including vulnerability/probability assessment, environmental health risk, the value of information, and redundancy reduction. The work starts with a formulation of a conceptual framework for groundwater quality monitoring to represent the methodology’s context. This work presents the development of Bayesian techniques for the assessment of groundwater quality. The primary aim is to develop a predictive model and a computer system to assess and predict the impact of pollutants on the water column. The process of the analysis begins by postulating a model in light of all available knowledge taken from relevant phenomenon. The previous knowledge as represented by the prior distribution of the model parameters is then combined with the new data through Bayes’ theorem to yield the current knowledge represented by the posterior distribution of model parameters. This process of updating information about the unknown model parameters is then repeated in a sequential manner as more and more new information becomes available.

KEYWORDS

Bayesian Belief Networks, Water Quality Assessment, Data Mining

Original Source URL: https://airccse.org/journal/ijsc/papers/5214ijsc03.pdf

https://airccse.org/journal/ijsc/current2014.html

A Review on Text Mining in Data Mining

Yogapreethi.N1, Maheswari.S2

1M.E.Scholar, Department of Computer Science & Engineering, Nandha Engineering College, Erode-638052, Tamil Nadu, India

2Associate Professor, Department of Computer Science & Engineering, Nandha Engineering College, Erode-638052, Tamil Nadu, India

ABSTRACT

Data mining is the knowledge discovery in databases and the gaol is to extract patterns and knowledge from large amounts of data. The important term in data mining is text mining. Text mining extracts the quality information highly from text. Statistical pattern learning is used to high quality information. High –quality in text mining defines the combinations of relevance, novelty and interestingness. Tasks in text mining are text categorization, text clustering, entity extraction and sentiment analysis. Applications of natural language processing and analytical methods are highly preferred to turn text into data for analysis. This survey is about the various techniques and algorithms used in text mining.

KEYWORDS

Data mining, Text mining, knowledge discovery

Original Source URL: https://aircconline.com/ijsc/V7N3/7316ijsc01.pdf

https://airccse.org/journal/ijsc/current2016.html

EVOLVING EFFICIENT CLUSTERING AND CLASSIFICATION PATTERNS IN LYMPHOGRAPHY DATA THROUGH DATA MINING TECHNIQUES

EVOLVING EFFICIENT CLUSTERING AND CLASSIFICATION PATTERNS IN LYMPHOGRAPHY DATA THROUGH DATA MINING TECHNIQUES 

Shomona Gracia Jacob1 and R.Geetha Ramani2 1Department of Computer Science and Engineering, Rajalakshmi Engineering College (Affiliated to Anna University, Chennai) graciarun@gmail.com 2Department of Information Science and Technology, College of Engineering, Guindy, Anna University, Chennai. rgeetha@yahoo.com

ABSTRACT 

Data mining refers to the process of retrieving knowledge by discovering novel and relative patterns from large datasets. Clustering and Classification are two distinct phases in data mining that work to provide an established, proven structure from a voluminous collection of facts. A dominant area of modern-day research in the field of medical investigations includes disease prediction and malady categorization. In this paper, our focus is to analyze clusters of patient records obtained via unsupervised clustering techniques and compare the performance of classification algorithms on the clinical data. Feature selection is a supervised method that attempts to select a subset of the predictor features based on the information gain. The Lymphography dataset comprises of 18 predictor attributes and 148 instances with the class label having four distinct values. This paper highlights the accuracy of eight clustering algorithms in detecting clusters of patient records and predictor attributes and highlights the performance of sixteen classification algorithms on the Lymphography dataset that enables the classifier to accurately perform multi-class categorization of medical data. Our work asserts the fact that the Random Tree algorithm and the Quinlan’s C4.5 algorithm give 100 percent classification accuracy with all the predictor features and also with the feature subset selected by the Fisher Filtering feature selection algorithm.. It is also stated here that the Density Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm offers increased clustering accuracy in less computation time. 

KEYWORDS 

Data mining, Clustering, Feature Selection, Classification, Lymphography Data   

ORIGINAL SOURCE URL : http://airccse.org/journal/ijsc/papers/3312ijsc09.pdf

http://airccse.org/journal/ijsc/current2012.html