DEEP LEARNING FOR DATA MINING UNSUPERVISED FEATURE LEARNING AND REPRESENTATION

Komal · Dr. Mukesh Soni · Dr. Bhushan M. Nanche · Dr. Haewon Byeon

Dec 2024 · Xoffencer International Book Publication House

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Data mining is the process of discovering patterns, correlations, and insights from large sets of data using various analytical techniques. It plays a crucial role in transforming raw data into meaningful information, which can then be used for decision-making, predictions, and insights in various fields such as business, healthcare, finance, and more. The most commonly used data mining techniques include classification, clustering, association, regression, anomaly detection, and sequential pattern mining. Each of these techniques has its own strengths and applications depending on the type of data and the goals of the analysis. Classification is one of the most popular techniques used in data mining. It involves categorizing data into predefined classes based on certain attributes. Algorithms such as decision trees, random forests, support vector machines, and neural networks are widely used for classification tasks. For instance, in the healthcare industry, classification techniques can be used to predict whether a patient is likely to develop a certain disease based on historical medical data. This technique works by training a model on a labeled dataset, where the outcome is known, and then using that model to classify new, unlabeled data into one of the predefined categories. Clustering is another essential data mining technique, where the goal is to group similar data points into clusters or segments. Unlike classification, clustering is an unsupervised learning method, meaning it doesn’t rely on predefined labels. Instead, it seeks to identify natural groupings in the data. Clustering algorithms like k-means, hierarchical clustering, and DBSCAN are commonly used. This technique is widely applied in market segmentation, where businesses group customers with similar behavior or preferences into clusters to better target marketing efforts. Clustering can also be useful in anomaly detection, where outliers that don’t fit well into any cluster can signal potential fraud or irregular behavior.

About the author

Komal Lecturer Technical education department Lucknow. She has more than 14 years of teaching experience in teaching. Recently Completed AI and data science course from IIT Madras. Worked as Assistant Professor in AKTU Lucknow and published more than 10 research papers in the field of image processing, pattern recognition and computational intelligence. Published 3 patents in image processing and computational intelligence. Recently awarded as best educational icon rewards in the field of education department and actively participated in various activities and responsibilities provided by the department

Dr. Mukesh Soni is a Assistant Professor in the Department of Computer Science Engineering, Dr. D. Y. Patil Vidyapeeth, Pune, Dr. D. Y. Patil School of Science & Technology, Tathawade, Pune, India. He is a Senior Member in IEEE. He has Qualified GATE examination in the year of 2012,2013,2014,2015,2018, and 2020 and got India Book of Record for this in 2020, also qualified UGC NET examination in 2014. His research interests include Applied Cryptography, Information Security, and Network Security. He has published many papers in peer review journals like IEEE Transactions, Elsevier, Springer. He has published 9 Indian Patents and 9 International Patents. He has received a total of 9 Awards like the Young Scientist awards, Young faculty award, Best faculty award, International Goal Achiever Award, NPTEL start awards, NPTEL believer award, Award Appreciation for Excellent performance in the field of Computer Science & Engineering, Award for Contribution to Student Development by different organizations. He is associated as a member reviewer in different peer-reviewed journals. He is also a member of many International and National professional bodies like IEEE, ACM Asia Society of Researcher, Scientific and Technical Research Association (STRA), "International Association of Engineers Institute for Engineering Research and Publication, Scholars Academic & Scientific Society.

Dr. Bhushan M. Nanche is an Assistant Professor in the Department of Information Technology at D.Y. Patil College of Engineering in Akurdi, Pune, Maharashtra, India, where he has been teaching since 2008. He earned his Bachelor's degree from Shivaji University, Kolhapur, in 2008, followed by a Master's in Computer Engineering from the University of Pune in 2015. Dr. Nanche has notable contributions in the form of patents and publications, with research interests that span the Internet of Things, Artificial Intelligence, and Algorithms. His work reflects a commitment to advancing technology and education in the field of information technology

Dr. Haewon Byeon received the Dr. degree in Biomedical Science from Ajou University School of Medicine. Haewon Byeon currently works at the Department of Medical Big Data, Inje University. His recent interests focus on health promotion, AImedicine, and biostatistics. He is currently a member of international committee for a Frontiers in Psychiatry, and an editorial board for World Journal of Psychiatry. Also, He were worked on a 4 projects (Principal Investigator) from the Ministryof Education, the Korea Research Foundation, and the Ministry of Health and Welfare. Byeon has published more than 343 articles and 19 books.

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