Introduction to Algorithms for Data Mining and Machine Learning

This series aims to capture new developments and applications in data mining and knowledge discovery, while summarizing the computational tools and techniques useful in data analysis. This series encourages the integration of mathematical, statistical, and computational methods and techniques through the publication of a broad range of textbooks, reference works, and handbooks. The inclusion of concrete examples and applications is highly encouraged. The scope of the series includes, but is not limited to, titles in the areas of data mining and knowledge discovery methods and applications, modeling, algorithms, theory and foundations, data and knowledge visualization, data mining systems and tools, and privacy and security issues.

DATA MINING: CONCEPTS AND TECHNIQUES 3RD EDITION] 13 society, science and engineering, medicine, and almost every other aspect of daily life. This explosive growth of available data volume is a result of the computerization of our society and the fast development of powerful data collection and storage tools. Businesses worldwide generate gigantic data sets, including sales transactions, stock trading records, product descriptions, sales promotions, company profiles and performance, and customer feedback. For example, large stores, such as Wal-Mart, handle hundreds of millions of transactions per week at thousands of branches around the world. Scientific and engineering practices generate high orders of petabytes of data in a continuous manner, from remote sensing, process measuring, scientific experiments, system performance, engineering observations, and environment surveillance.

This paper aims to cover the different machine learning algorithms. These algorithms can be used in the different fields of data mining, image processing, predictive analysis and many more.

Undergraduate topics in computer science, 2016

'Undergraduate Topics in Computer Science' (UTiCS) delivers high-quality instructional content for undergraduates studying in all areas of computing and information science. From core foundational and theoretical material to final-year topics and applications, UTiCS books take a fresh, concise, and modern approach and are ideal for self-study or for a one-or two-semester course. The texts are all authored by established experts in their fields, reviewed by an international advisory board, and contain numerous examples and problems, many of which include fully worked solutions.

In this paper we have to focus on data mining concept and its tools and technology which help us for market perspective to take a proper decision and get a proper result. Data mining is a logical process that is used to analyze large amounts of information that can be in the form of document in order to find important data. The goal of data mining is to find patterns that were previously unknown. Once you have found out those patterns, you can use them to solve number of complex problems. Data mining [sometimes called data or knowledge discovery from data (KDD)] is the process of analyzing data from huge amount of data and summarizing it into useful information. Data mining is one of a number of analytical tools for analyzing data. It allows users to search and analyze data from many different source and transform into decision making data from which user can take decision. IT is the process of finding patterns among dozens of fields in large relational databases. Data mining is a powerful tool because it can provide relevant information. But it is not so easy to get relevant information that can help you to take proper decision. This is where data mining becomes a powerful tool that will help to extract useful information. Keywords:- Data Mining, KDD, Data Mining Task, Data Preprocessing, Visualization of the data mining model, Data Mining: classification, methods and its application

—With an enormous increase in data stored in data warehouses and databases, it is important to develop tools which could analyze data and refine required knowledge from it. This paper provides an overview of data mining and how to extract a basic knowledge. This paper also gives review about various applications where data mining can be effective to improve their business and get excellent results. Even we have discussed some future trends where data mining can be proved effective in various fields.

International Journal of Engineering Research and, 2015

In this today's generation enormous amount of data stored in databases and data warehouses, for analysis the stored data for business intelligence to decision making, becomes difficult. Data mining is a process of deriving knowledge from such a huge data. In this article a summarized report on the data mining and its essential algorithms are categorized.

INFORMATICA (LJUBLJANA), vol. 29, pp. 1-2, 2005

Informatica 29 (2005) 1-2 Informatica 29 (2005) 1-2 J. Abonyi et al.

Artificial Intelligence, 2021

Jaydip Sen is associated with Praxis Business School, Kolkata, India, as a professor in the Department of Data Science. His research areas include security and privacy issues in computing and communication, intrusion detection systems, machine learning, deep learning, and artificial intelligence in the financial domain. He has more than 200 publications in reputed international journals, refereed conference proceedings, and 18 book chapters in books published by internationally renowned publishing houses, such as Springer, CRC press, IGI Global, etc. Currently, he is serving on the editorial board of the prestigious journal Frontiers in Communications and Networks and in the technical program committees of a number of high-ranked international conferences organized by the IEEE, USA, and the ACM, USA. He has been listed among the top 2% of scientists in the world for both the years 2020 and 2021 as of August 2021.

2011

Abstract Data mining has become a well-established discipline within the domain of artificial intelligence (AI) and knowledge engineering (KE). It has its roots in machine learning and statistics, but encompasses other areas of computer science. It has received much interest over the last decade as advances in computer hardware have provided the processing power to enable large-scale data mining to be conducted.

2013

Machine learning [1] is concerned with algorithmically finding patterns and relationships in data, and using these to perform tasks such as classification and prediction in various domains. We now introduce some relevant terminology and provide an overview of a few sorts of machine learning approaches.