{"product_id":"compression-schemes-for-mining-large-datasets-a-machine-learning-perspective-paperback","title":"Compression Schemes for Mining Large Datasets: A Machine Learning Perspective - Paperback","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eT. Ravindra Babu\u003c\/b\u003e (Author), \u003cb\u003eM. Narasimha Murty\u003c\/b\u003e (Author), \u003cb\u003eS. V. Subrahmanya\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003eThis book addresses the challenges of data abstraction generation using a least number of database scans, compressing data through novel lossy and non-lossy schemes, and carrying out clustering and classification directly in the compressed domain. Schemes are presented which are shown to be efficient both in terms of space and time, while simultaneously providing the same or better classification accuracy. Features: describes a non-lossy compression scheme based on run-length encoding of patterns with binary valued features; proposes a lossy compression scheme that recognizes a pattern as a sequence of features and identifying subsequences; examines whether the identification of prototypes and features can be achieved simultaneously through lossy compression and efficient clustering; discusses ways to make use of domain knowledge in generating abstraction; reviews optimal prototype selection using genetic algorithms; suggests possible ways of dealing with big data problems using multiagent systems.\u003c\/p\u003e\u003ch3\u003eBack Jacket\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eAs data mining algorithms are typically applied to sizable volumes of high-dimensional data, these can result in large storage requirements and inefficient computation times.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eThis unique text\/reference addresses the challenges of data abstraction generation using a least number of database scans, compressing data through novel lossy and non-lossy schemes, and carrying out clustering and classification directly in the compressed domain. Schemes are presented which are shown to be efficient both in terms of space and time, while simultaneously providing the same or better classification accuracy, as illustrated using high-dimensional handwritten digit data and a large intrusion detection dataset.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eTopics and features: \u003c\/b\u003e \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003ePresents a concise introduction to data mining paradigms, data compression, and mining compressed data\u003c\/li\u003e\n\u003cli\u003eDescribes a non-lossy compression scheme based on run-length encoding of patterns with binary valued features\u003c\/li\u003e\n\u003cli\u003eProposes a lossy compression scheme that recognizes a pattern as a sequence of features and identifying subsequences\u003c\/li\u003e\n\u003cli\u003eExamines whether the identification of prototypes and features can be achieved simultaneously through lossy compression and efficient clustering\u003c\/li\u003e\n\u003cli\u003eDiscusses ways to make use of domain knowledge in generating abstraction\u003c\/li\u003e\n\u003cli\u003eReviews optimal prototype selection using genetic algorithms\u003c\/li\u003e\n\u003cli\u003eSuggests possible ways of dealing with big data problems using multiagentsystems \u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003eA must-read for all researchers involved in data mining and big data, the book proposes each algorithm within a discussion of the wider context, implementation details and experimental results. These are further supported by bibliographic notes and a glossary\u003ci\u003e.\u003c\/i\u003e\u003c\/p\u003e\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eDr. T. Ravindra Babu\u003c\/b\u003e is a Principal Researcher in the E-Commerce Research Labs at Infosys Ltd., Bangalore, India. \u003cb\u003eMr. S.V. Subrahmanya\u003c\/b\u003e is Vice President and Research Fellow at the same organization. \u003cb\u003eDr. M. Narasimha Murty\u003c\/b\u003e is a Professor in the Department of Computer Science and Automation at the Indian Institute of Science, Bangalore, India.\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 197\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.46 x 9.21 x 6.14 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eIllustrated:\u003c\/strong\u003e Yes\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e September 17, 2016\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":48405522317561,"sku":"9781447170556","price":89.08,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0789\/2782\/3097\/files\/FwYrtA2olg9781447170556.webp?v=1778842619","url":"https:\/\/bookscloud.io\/products\/compression-schemes-for-mining-large-datasets-a-machine-learning-perspective-paperback","provider":"BooksCloud Book Dropshipping","version":"1.0","type":"link"}