CLUSTERING AND CLASSIFICATION TECHNIQUES USING TEXT MINING
| Main Author: | VarshaC. Pande*1, Dr. Harshala B. Pethe2 & Dr. Abha. S. Khandelwal3 |
|---|---|
| Format: | Article |
| Terbitan: |
, 2018
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| Subjects: | |
| Online Access: |
https://zenodo.org/record/1483957 |
Daftar Isi:
- The text is nothing but the combination of characters. Therefore, analyzing and extracting information patterns from such data sets are more complex. Several methods have been proposed for analyzing such texts and extracting information.Data mining, a specific area named text mining is used to classify the huge semi structured or unstructured data needs proper clustering. Maximum text documents involves fast retrieval of information, arrangement of documents, exploring of information from the documents. Declaration of text input data and classification of the documents is a complex process. Text Clustering is an unsupervised method in which no input out patterns is predefined. This method is based upon the idea of dividing the similar text into the same cluster. Individual cluster consists of number of records. The clustering is thought better if the contents of documents of intra cluster are more alike than the contents of inter-cluster documents. Classificationis used to find out in which group each data instance is related within a given dataset. It is used for classifying data into different classes according to some constrains. Several major kinds of classification algorithms including C4.5, ID3, k-nearest neighbor classifier, Naive Bayes, SVM, and ANN are used for classification. This paper describes the comparative study of clustering and Classification Algorithms.