{"product_id":"9789819667901","title":"Hands-on Pattern Mining: Theory and Examples with PAMI, Sklearn, Keras, and TensorFlow","description":"\u003ch1\u003eHands-on Pattern Mining: Theory and Examples with PAMI, Sklearn, Keras, and TensorFlow\u003c\/h1\u003e \u003ch2\u003eRage, Uday Kiran\u003c\/h2\u003e \u003cp\u003e\u003c\/p\u003e\u003cp class=\"MsoNormal\" style=\"margin-bottom: 0in; line-height: normal; background: white;\"\u003e\u003cspan style=\"font-size: 11.0pt; font-family: 'Calibri',sans-serif; mso-fareast-font-family: 'Times New Roman'; color: #222222; mso-font-kerning: 0pt; mso-ligatures: none;\"\u003eThis book introduces pattern mining by presenting various pattern mining techniques and giving hands-on experience with each technique. Pattern mining is a popular data mining technique with many real-world applications, and involves discovering all user interest-based patterns that may exist in a database. Several models and numerous algorithms were described in the literature to find these patterns in binary databases, quantitative databases, uncertain databases, and streams. Since the lack of a Python toolkit containing these algorithms has limited the wide adaptability of pattern-mining techniques, the author developed Pattern Mining (PAMI) Python library, which currently contains 80+ algorithms to discover useful patterns in transactional databases, temporal databases, quantitative databases, and graphs.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"MsoNormal\" style=\"margin-bottom: 0in; line-height: normal; background: white;\"\u003e\u003cspan style=\"font-size: 11.0pt; font-family: 'Calibri',sans-serif; mso-fareast-font-family: 'Times New Roman'; color: #222222; mso-font-kerning: 0pt; mso-ligatures: none;\"\u003eThe book consists of three main parts:\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"MsoNormal\" style=\"text-indent: -.25in; line-height: normal; mso-list: l0 level1 lfo1; tab-stops: list .5in; background: white; margin: 0in 0in 0in 47.25pt;\"\u003e\u003c!-- [if !supportLists]--\u003e\u003cspan style=\"font-size: 10.0pt; mso-bidi-font-size: 12.0pt; font-family: Symbol; mso-fareast-font-family: Symbol; mso-bidi-font-family: Symbol; color: #222222; mso-font-kerning: 0pt; mso-ligatures: none;\"\u003e\u003cspan style=\"mso-list: Ignore;\"\u003e·\u003cspan style=\"font: 7.0pt 'Times New Roman';\"\u003e \u003c\/span\u003e\u003c\/span\u003e\u003c\/span\u003e\u003c!--[endif]--\u003e\u003cspan style=\"font-size: 11.0pt; font-family: 'Calibri',sans-serif; mso-fareast-font-family: 'Times New Roman'; color: #222222; mso-font-kerning: 0pt; mso-ligatures: none;\"\u003eIntroduction: The first chapter introduces big data, types of learning techniques, and the importance of pattern mining. The second chapter introduces the PAMI library, its organizational structure, installation, and usage.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"MsoNormal\" style=\"text-indent: -.25in; line-height: normal; mso-list: l0 level1 lfo1; tab-stops: list .5in; background: white; margin: 0in 0in 0in 47.25pt;\"\u003e\u003c!-- [if !supportLists]--\u003e\u003cspan style=\"font-size: 10.0pt; mso-bidi-font-size: 12.0pt; font-family: Symbol; mso-fareast-font-family: Symbol; mso-bidi-font-family: Symbol; color: #222222; mso-font-kerning: 0pt; mso-ligatures: none;\"\u003e\u003cspan style=\"mso-list: Ignore;\"\u003e·\u003cspan style=\"font: 7.0pt 'Times New Roman';\"\u003e \u003c\/span\u003e\u003c\/span\u003e\u003c\/span\u003e\u003c!--[endif]--\u003e\u003cspan style=\"font-size: 11.0pt; font-family: 'Calibri',sans-serif; mso-fareast-font-family: 'Times New Roman'; color: #222222; mso-font-kerning: 0pt; mso-ligatures: none;\"\u003ePattern mining algorithms and examples: The following chapters present the state-of-the-art techniques for discovering user interest-based patterns in (1) transactional databases, (2) temporal databases, (3) quantitative databases, (4) uncertain databases, (5) sequential databases, and (6) graphs.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"MsoNormal\" style=\"text-indent: -.25in; line-height: normal; mso-list: l0 level1 lfo1; tab-stops: list .5in; background: white; margin: 0in 0in 0in 47.25pt;\"\u003e\u003c!-- [if !supportLists]--\u003e\u003cspan style=\"font-size: 10.0pt; mso-bidi-font-size: 12.0pt; font-family: Symbol; mso-fareast-font-family: Symbol; mso-bidi-font-family: Symbol; color: #222222; mso-font-kerning: 0pt; mso-ligatures: none;\"\u003e\u003cspan style=\"mso-list: Ignore;\"\u003e·\u003cspan style=\"font: 7.0pt 'Times New Roman';\"\u003e \u003c\/span\u003e\u003c\/span\u003e\u003c\/span\u003e\u003c!--[endif]--\u003e\u003cspan style=\"font-size: 11.0pt; font-family: 'Calibri',sans-serif; mso-fareast-font-family: 'Times New Roman'; color: #222222; mso-font-kerning: 0pt; mso-ligatures: none;\"\u003eApplications: The book concludes with several applications, where the predicted knowledge using TensorFlow and PyTorch was transformed into a database to discover future trends or patterns.\u003c\/span\u003e\u003c\/p\u003e \u003ch3\u003eDetails\u003c\/h3\u003e \u003cp\u003ePublished by: Springer\u003c\/p\u003e \u003cp\u003ePublication Date: 2025-07-11\u003c\/p\u003e \u003cp\u003eFormat: Hardcover\u003c\/p\u003e \u003cp\u003eISBN-13: 9789819667901\u003c\/p\u003e \u003cp\u003eDOI: 10.1007\/978-981-96-6791-8\u003c\/p\u003e \u003cp\u003eDimensions: 235cm x155cm\u003c\/p\u003e \u003cp\u003ePages: 182\u003c\/p\u003e ","brand":"Springer Nature Singapore","offers":[{"title":"Default Title","offer_id":44309898068108,"sku":"9789819667901","price":58.49,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9789819667901_0a67d435-3edc-4a66-82b4-051047233fd4.jpg?v=1775066293","url":"https:\/\/lateknightbooks.com\/products\/9789819667901","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}