scholarly journals Text Categorization based on Associative Classification

2010 ◽  
pp. 115-121
Author(s):  
Padmavati Shrivastava ◽  
Uzma Ansari
Keyword(s):  
Text Classification ◽  
Association Rule ◽  
Association Rule Mining ◽  
Text Categorization ◽  
Low Cost ◽  
Classification Rule ◽  
Associative Classification ◽  
Rule Mining ◽  
Text Documents ◽  
Practical Applications

Text mining is an emerging technology that can be used to augment existing data in corporate databases by making unstructured text data available for analysis. The incredible increase in online documents, which has been mostly due to the expanding internet, has renewed the interest in automated document classification and data mining. The demand for text classification to aid the analysis and management of text is increasing. Text is cheap, but information, in the form of knowing what classes a text belongs to, is expensive. Text classification is the process of classifying documents into predefined categories based on their content. Automatic classification of text can provide this information at low cost, but the classifiers themselves must be built with expensive human effort, or trained from texts which have themselves been manually classified. Both classification and association rule mining are indispensable to practical applications. For association rule mining, the target of discovery is not pre-determined, while for classification rule mining there is one and only one predetermined target. Thus, great savings and conveniences to the user could result if the two mining techniques can somehow be integrated. In this paper, such an integrated framework, called associative classification is used for text categorization The algorithm presented here for text classification uses words as features , to derive feature set from preclassified text documents. The concept of Naïve Bayes classifier is then used on derived features for final classification.

scholarly journals A Review on associative classification for Diabetic Datasets A Simulation Approach

2013 ◽  
Vol 7 (1) ◽  
pp. 533-538
Author(s):  
Deepti Jain ◽  
Divakar Singh
Keyword(s):  
Neural Network ◽  
Association Rules ◽  
Classification System ◽  
Association Rule ◽  
Association Rule Mining ◽  
Review Paper ◽  
Classification Rule ◽  
Associative Classification ◽  
Rule Mining ◽  
The Neural Network

Association rules are used to discover all the interesting relationship in a potentially large database. Association rule mining is used to discover a small set of rules over the database to form more accurate evaluation. They capture all possible rules that explain the presence of some attributes in relation to the presence of other attributes. This review paper aims to study and observe a flexible way, of, mining frequent patterns by extending the idea of the Associative Classification method. For better performance, the Neural Network Association Classification system is also analyzed here to be one of the approaches for building accurate and efficient classifiers. In this review paper, the Neural Network Association Classification system is studied and compared in order to find best possible accurate results. Association rule mining and classification rule mining can be integrated to form a framework called as Associative Classification and these rules are referred as Class Association Rules. This review research paper also analyzes how data mining techniques are used for predicting different types of diseases. This paper reviewed the research papers which mainly concentrated on predicting Diabetes.

An Approach for Arabic Text Categorization Using Association Rule Mining

2011 ◽  
Vol 23 (01) ◽  
pp. 81-106 ◽  
Author(s):  
QASEM A. AL-RADAIDEH ◽  
EMAD M. AL-SHAWAKFA ◽  
ABDULLAH S. GHAREB ◽  
HANI ABU-SALEM
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Text Categorization ◽  
Arabic Text ◽  
Rule Mining

NEW DISCRETE CROW SEARCH ALGORITHM FOR CLASS ASSOCIATION RULE MINING

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Search Algorithm ◽  
Classification Problem ◽  
Discrete Version ◽  
Classification Algorithms ◽  
Associative Classification ◽  
Rule Mining ◽  
Rule Based ◽  
Class Association Rule

Associative Classification (AC) or Class Association Rule (CAR) mining is a very efficient method for the classification problem. It can build comprehensible classification models in the form of a list of simple IF-THEN classification rules from the available data. In this paper, we present a new, and improved discrete version of the Crow Search Algorithm (CSA) called NDCSA-CAR to mine the Class Association Rules. The goal of this article is to improve the data classification accuracy and the simplicity of classifiers. The authors applied the proposed NDCSA-CAR algorithm on eleven benchmark dataset and compared its result with traditional algorithms and recent well known rule-based classification algorithms. The experimental results show that the proposed algorithm outperformed other rule-based approaches in all evaluated criteria.

NEW DISCRETE CROW SEARCH ALGORITHM FOR CLASS ASSOCIATION RULE MINING

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Search Algorithm ◽  
Classification Problem ◽  
Discrete Version ◽  
Classification Algorithms ◽  
Associative Classification ◽  
Rule Mining ◽  
Rule Based ◽  
Class Association Rule

Associative Classification (AC) or Class Association Rule (CAR) mining is a very efficient method for the classification problem. It can build comprehensible classification models in the form of a list of simple IF-THEN classification rules from the available data. In this paper, we present a new, and improved discrete version of the Crow Search Algorithm (CSA) called NDCSA-CAR to mine the Class Association Rules. The goal of this article is to improve the data classification accuracy and the simplicity of classifiers. The authors applied the proposed NDCSA-CAR algorithm on eleven benchmark dataset and compared its result with traditional algorithms and recent well known rule-based classification algorithms. The experimental results show that the proposed algorithm outperformed other rule-based approaches in all evaluated criteria.

AN ASSOCIATION RULE MINING APPROACH FOR CO-REGULATED SIGNATURE GENES IDENTIFICATION IN CANCER

2009 ◽  
Vol 18 (08) ◽  
pp. 1409-1423 ◽  
Author(s):  
K. R. SEEJA ◽  
M. A. ALAM ◽  
S. K. JAIN
Keyword(s):  
Gene Expression ◽  
Association Rule ◽  
Association Rule Mining ◽  
Low Cost ◽  
Published Data ◽  
Cancer Tissue ◽  
Rule Mining ◽  
Signature Genes ◽  
Microarray Chips ◽  
Sage Data

When a normal cell becomes cancerous there will be change in expression of many genes in that cell. Identification of these changes in gene expression in cancer tissue may lead to the development of novel tools for early diagnosis and effective therapeutics. In this paper we present an association rule mining approach to identify the association between the genes that are differentially expressed in cancer tissue compared to normal tissue. We design an association rule mining algorithm GeneExpMiner for gene expression data mining. Serial Analysis of Gene Expression (SAGE) data related to pancreas cancer is used to demonstrate the approach. It is expected that the approach will help in developing better treatment methodologies for cancer and designing low cost microarray chips for diagnosing cancer. The results have been validated in terms of Gene Ontology and the signature genes that we have identified are matching with the published data.

scholarly journals Prediction of By-Diseases in Diabetic Patients Using Associative Classification with Improved Classifier Accuracy for Decision Support System

2019 ◽  
Vol 8 (6) ◽  
pp. 2625-2628
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Diabetic Patients ◽  
Classification Algorithms ◽  
Data Sets ◽  
Classification Models ◽  
Associative Classification ◽  
Rule Mining ◽  
Data Mining Technique ◽  
Mining Technique

Associative Classification in data mining technique formulates more and more simple methods and processes to find and predict the health problems like diabetes, tumors, heart problems, thyroid, cancer, malaria etc. The methods of classification combined with association rule mining gradually helps to predict large amount of data and also builds the accurate classification models for the future analysis. The data in medical area is sometimes vast and containss the information that relates to different diseases. It becomes difficult to estimate and analyze the disease problems that change from period to period based on severity. In this research paper, the use and need of associative classification for the medical data sets and the application of associative classification on the data in order to predict the by-diseases has been put front. The association rules in this context developed in training phase of data have predicted the chance of occurrence of other diseases in persons suffering with diabetes mellitus using Predictive Apriori. The associative classification algorithms like CAR is deployed in the context of accuracy measures.

Sign in / Sign up

Export Citation Format

Share Document