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dc.contributor.supervisorGoel, Shivani-
dc.contributor.authorKumar, Suresh-
dc.descriptionM.E.-CSE Part Time-Thesisen
dc.description.abstractWith 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.en
dc.description.sponsorshipComputer Science and Engineering, Thapar University, Patialaen
dc.format.extent1312683 bytes-
dc.subjectText miningen
dc.subjectText classificationen
dc.subjectFeature extractionen
dc.subjectTerm weightingen
dc.subjectLinear SVCen
dc.subjectK meansen
dc.subjectcomputer scienceen
dc.titleOptimization of Text Classification Using Supervised and Unsupervised Learning Approachen
Appears in Collections:Masters Theses@CSED

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