Introduction to Data Mining

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In this report we review and compare data mining methods and algorithms. After ashort introduction on the general concepts of data mining we focus on four specic topics, metaquerying, data clustering, similarity queries and visualization, and go deeper, analyzingthe various approaches and proposals known in the literature and, where applicable, in themarket. In particular, in each of the four parts an eort is made tond out a common modelto compare results and applicability.

Academic Press/Elsevier, 2019

Introduction to Algorithms for Data Mining and Machine Learning (book) introduces the essential ideas behind all key algorithms and techniques for data mining and machine learning, along with optimization techniques. Its strong formal mathematical approach, well selected examples, and practical software recommendations help readers develop confidence in their data modeling skills so they can process and interpret data for classification, clustering, curve-fitting and predictions. Masterfully balancing theory and practice, it is especially useful for those who need relevant, well explained, but not rigorous (proofs based) background theory and clear guidelines for working with big data.

Data mining is a process which finds useful patterns from large amount of data. The paper discusses few of the data mining techniques, algorithms and some of the organizations which have adapted data mining technology to improve their businesses and found excellent results.

SIGMOD Record

Basic Concepts for Beginners. The evolution of database technology is an essential prerequisite for understanding the need of knowledge discovery in databases (KDD). This evolution is described in the book to present data mining as a natural stage in the data processing history: we ...

—Data mining an non-trivial extraction of novel, implicit, and actionable knowledge from large data sets is an evolving technology which is a direct result of the increasing use of computer databases in order to store and retrieve information effectively .It is also known as Knowledge Discovery in Databases (KDD) and enables data exploration, data analysis, and data visualization of huge databases at a high level of abstraction, without a specific hypothesis in mind. The working of data mining is understood by using a method called modeling with it to make predictions. Data mining techniques are results of long process of research and product development and include artificial neural networks, decision trees and genetic algorithms. This paper surveys the data mining technology, its definition, motivation, its process and architecture, kind of data mined, functionalities and classification of data mining, major issues, applications and directions for further research of data mining technology.

Data mining may be regarded as the process of discovering insightful and predictive models from massive data. It is the art of extracting useful information from large amounts of data. It combines traditional data analysis with sophisticated algorithms for processing large amount of data. It is an interdisciplinary field merging concepts from database systems, statistics, machine learning, computing, information theory, and pattern recognition. It has the real potential of becoming part of electrical engineering education. The main objective of this paper is to provide a brief introduction to data mining.

Data mining is the computational process of discovering patterns in large data sets. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. The paper discusses few of the data mining techniques, algorithms and some of the organizations which have adapted data mining technology to improve their businesses and found excellent results and focuses on presenting the applications of data mining in the business environment.

International journal of engineering research and technology, 2018

Data mining is a rapidly growing field which has wide applications in variety of fields. It is a multi-disciplinary field which integrates statistics, neural networks, machine learning, visualization etc. This paper is an attempt to briefly review the various tools and techniques used in data mining. The paper also reviews some of the important applications of data mining in various areas.