Practical Data Mining Techniques and Applications

Data mining techniques and algorithms are extensively used to build real-world applications. A practical approach can be applied to data mining techniques to build applications. Once deployed, an application enables the developers to work on the users' goals and mold the algorithms with respect to users' perspectives.

Practical Data Mining Techniques and Applications focuses on various concepts related to data mining and how these techniques can be used to develop and deploy applications. The book provides a systematic composition of fundamental concepts of data mining blended with practical applications. The aim of this book is to provide access to practical data mining applications and techniques to help readers gain an understanding of data mining in practice. Readers also learn how relevant techniques and algorithms are applied to solve problems and to provide solutions to real-world applications in different domains. This book can help academicians to extend their knowledge of the field as well as their understanding of applications based on different techniques to gain greater insight. It can also help researchers with real-world applications by diving deeper into the domain. Computing science students, application developers, and business professionals may also benefit from this examination of applied data science techniques.

By highlighting an overall picture of the field, introducing various mining techniques, and focusing on different applications and research directions using these methods, this book can motivate discussions among academics, researchers, professionals, and students to exchange and develop their views regarding the dynamic field that is data mining.

數據挖掘技術和算法被廣泛應用於構建實際應用程序。可以將實用方法應用於數據挖掘技術以構建應用程序。一旦部署，應用程序使開發人員能夠根據用戶的目標和觀點來塑造算法。

《實用數據挖掘技術與應用》專注於與數據挖掘相關的各種概念以及如何使用這些技術來開發和部署應用程序。該書系統地結合了數據挖掘的基本概念和實際應用。本書的目的是提供實用的數據挖掘應用和技術，幫助讀者了解實際應用中的數據挖掘。讀者還可以了解相關技術和算法如何應用於解決問題，並為不同領域的實際應用提供解決方案。本書可以幫助學者擴展他們對該領域的知識，以及對基於不同技術的應用的理解，以獲得更深入的洞察。它還可以幫助研究人員深入研究領域的實際應用。計算科學學生、應用程序開發人員和商業專業人士也可以從這本應用數據科學技術的研究中受益。

通過突出該領域的整體情況，介紹各種挖掘技術，並關注使用這些方法的不同應用和研究方向，本書可以激發學術界、研究人員、專業人士和學生之間的討論，交流和發展他們對數據挖掘這一動態領域的觀點。

Dr. Ketan Shah, Head of Information Technology Department, at SVKM's NMIMS - Mukesh Patel School of Technology Management and Engineering (MPSTME). He also holds the additional responsibility of Associate Dean (Accreditations) at MPSTME. In this role, he has successfully spearheaded the ABET accreditation of five programs at MPSTME. All the five programs have got ABET accreditation for six years up to 2026. He has his Bachelors and Master Degree in Electronics Engineering from Mumbai University and PhD in Engineering from NMIMS. He has 23 years of teaching experience and has taught undergraduate and postgraduate students. He has also guided 5 doctoral students who have successfully completed their PhD. His research area is Data Mining. He has 15 Journal papers and 25 conference papers to his credit.

Dr. Neepa Shah, Ex-Head of Information Technology Department, at Dwarkadas J. Sanghvi College of Engineering, Mumbai University. She is devoted to education, and always interested in applying new things. She graduated in the year 2001 from Pune University, completed her M. Tech. in 2008, and PhD in the year 2017 and has teaching experience of 20+ years (since 2001). Along with teaching different subjects and guiding students for placement and higher studies, she has played different roles simultaneously. To mention a few, vice-chair of software team, admission committee, program chair for NBA committee, technical chair for the international conference, ICACTA and DJ ASCII-State level project competition, and many more. She has also implemented a few inhouse software for DJSCE. She is a registered and recognized coach for the International Collegiate Programming Competition (ACM-ICPC) since 2006. She has also participated in 50+ workshops and STTPs. She has published 30+ papers in various international and national journals / conferences. She has received Research Grant in a. y. 2015-16 from University of Mumbai of Amount Rs. 25,000/-. She is also an award winner of "BEST FACULTY" in 2008. Her areas of specialization include Data Mining, Semantics, Natural Language Processing, Parallel Computing, and Distributed Systems. She has delivered special talks on advanced algorithms, semantics, and distributed document clustering in a few institutes.

Dr. Vinaya Sawant, Head of Information Technology Department of Dwarkadas J. Sanghvi College of Engineering, Mumbai University, possesses an experience of 19 years in teaching subjects such as Database Management Systems, Advanced Databases, Data Warehousing and Mining, Data Analytics, Distributed Systems, Image Processing. Apart from academic activities, she is also involved in administrative committees at the college level as well as actively interacted outside college in many ways. She was invited as reviewers in many International Conferences. Also, she has conducted many guest lectures in other Universities. She has successfully completed her Masters (M. Tech) in Computer Engineering in 2010 from NMIMS University and secured first place at the University level. She then completed her PhD degree from the same University, in the year 2020. Her research is directed towards proposing and implementing the distributed data mining algorithms that aims to reduce the execution time and communication overhead in a distributed environment, effectively and efficiently. Her work is highlighted in 25+ recognized international publications (Scopus indexed) such as IEEE, Elsevier, Springer, etc. Her active participation in conferences shows her ability to work in the current research areas and diving deep into it. She is more inclined towards the current trends of applied data mining and interested in contributing to that direction.

Dr. Neeraj Parolia is an Associate Professor in the Business Analytics and Technology Management Department at Towson University. He received his PhD from the University of Central Florida. His research interests include IT Project and Program Management. His research has been published in Information and Management, International Journal of Project Management, Information and Software Technology, Electronic Journal of Information Systems Evaluation, International Journal of Information Technology Project Management and International Journal of Project Organization and Management. Neeraj is an active participant in the AIS SIG of Information Technology Project Management and has served on the board of the American Society for the Advancement of Project Management as Director of Education.

Dr. Ketan Shah，是SVKM's NMIMS - Mukesh Patel School of Technology Management and Engineering (MPSTME)的資訊技術系主任。他還擔任MPSTME的副院長（認證）的額外責任。在這個角色中，他成功地領導了MPSTME五個課程的ABET認證。這五個課程已獲得ABET認證，有效期為2026年。他在孟買大學獲得了電子工程學士和碩士學位，並在NMIMS獲得了工程學博士學位。他擁有23年的教學經驗，曾教授本科和研究生學生。他還指導了5名成功完成博士學位的博士生。他的研究領域是數據挖掘。他發表了15篇期刊論文和25篇會議論文。

Dr. Neepa Shah，曾任孟買大學Dwarkadas J. Sanghvi工程學院資訊技術系主任。她致力於教育，並對應用新事物感興趣。她於2001年畢業於普納大學，於2008年獲得碩士學位，並於2017年獲得博士學位，擁有20多年（自2001年以來）的教學經驗。除了教授不同科目和指導學生就業和深造外，她還同時擔任不同的角色。其中包括軟件團隊副主席、招生委員會、NBA委員會的課程主席、國際會議ICACTA和DJ ASCII的技術主席，以及其他許多角色。她還為DJSCE實施了一些內部軟件。自2006年以來，她是國際大學生程式設計競賽（ACM-ICPC）的註冊和認可教練。她還參加了50多個研討會和STTP。她在各種國際和國內期刊/會議上發表了30多篇論文。她於2015-16學年從孟買大學獲得了2.5萬盧比的研究資助。她還於2008年獲得“最佳教師”獎。她的專業領域包括數據挖掘、語義學、自然語言處理、並行計算和分佈式系統。她曾在幾所學院就高級算法、語義學和分佈式文檔聚類等主題發表特別演講。

Dr. Vinaya Sawant，是孟買大學Dwarkadas J. Sanghvi工程學院資訊技術系的主任，擁有19年的教學經驗，教授的科目包括數據庫管理系統、高級數據庫、數據倉儲和挖掘、數據分析、分佈式系統、圖像處理等。除了學術活動外，她還參與學院層面的行政委員會，並在校外積極參與多種活動。她曾應邀擔任多個國際會議的審稿人，並在其他大學舉辦過多場客座講座。她於2010年從NMIMS大學獲得計算機工程碩士學位，並在2020年獲得博士學位，並在大學層面獲得第一名。她的研究方向是提出和實施分佈式數據挖掘算法，旨在在分佈式環境中有效地減少執行時間和通信開銷。她的工作在25多個國際公認的出版物（Scopus索引）中得到了突出的展示，如IEEE、Elsevier、Springer等。她在會議中的積極參與表明她在當前的研究領域中能夠工作並深入研究。她更傾向於應用數據挖掘的當前趨勢，並有興趣為該方向做出貢獻。

Dr. Neeraj Parolia是Towson大學商業分析和技術管理系的副教授。他在中佛羅里達大學獲得博士學位。他的研究興趣包括IT項目和P