Please use this identifier to cite or link to this item:
Title: Optimization of Text Classification Using Supervised and Unsupervised Learning Approach
Authors: Kumar, Suresh
Supervisor: Goel, Shivani
Keywords: Text mining;Text classification;Feature extraction;Term weighting;Linear SVC;SGD;K means;cse;computer science
Issue Date: 11-Aug-2015
Abstract: With the rapid growth of the Internet and the raise in on-line information, the technology for effective retrieval and categorization of large amounts of text data plays a vital role in text mining. In the 1990s, the concert of computers enhanced harshly and it became feasible to handle huge amount of text data. This has led to the utilization of machine learning approach, which is a method of exploring the structure and learning of algorithms that can be trained from and make predictions on data given in a category label. This approach provides brilliant precision, reduces effort, and ensures traditional utilization of resources. Due to rapid spread and high dimensionality of online information, efficient retrieval of some exact information is complicated without good indexing and summarization of document content. Therefore document categorization or classification may be the result to successfully handle and manage such large amount of text. Text Classification, also known as text categorization, is the task of automatically allocating unlabeled documents into predefined categories. Text Classification means allocating a document to one or more categories or classes. The ability to accurately perform a classification task depends on the representation of documents to be classified. Text representation transforms the textural documents into a compact format. Text Classification plays an important role in information mining, summarization, text recovery and question-answering. It uses several tools from information retrieval (IR) and Machine Learning. Here we are reviewing the effectiveness of different supervised and unsupervised learning approaches in text classification.
Description: M.E.-CSE Part Time-Thesis
Appears in Collections:Masters Theses@CSED

Files in This Item:
File Description SizeFormat 
3558.pdf1.29 MBAdobe PDFThumbnail

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.