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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.
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.
2005
Abstract Data Mining (DM) has enjoyed great popularity in recent years, with advances in both research and commercialization. The first generation of DM research and development has yielded several commercially available systems, both stand-alone and integrated with database systems; produced scalable versions of algorithms for many classical DM problems; and introduced novel pattern discovery problems.
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.
Knowledge discovery and data mining has recently emerged as an important research direction for extracting useful information from vast repositories of data of various types. This chapter discusses some of the basic concepts and issues involved in this process with special emphasis on different data mining tasks. The major challenges in data mining are mentioned. Finally, the recent trends in data mining are described and an extensive bibliography is provided.
International Journal of Computer Applications, 2015
Data mining is the process of extracting the useful data, patterns and trends from a large amount of data by using techniques like clustering, classification, association and regression. There are a wide variety of applications in real life. Various tools are available which supports different algorithms. A summary about data mining tools available and the supporting algorithms is the objective of this paper. Comparison between various tools has also been done to enable the users use various tools according to their requirements and applications. Different validation indices for the validation are also summarized.
Data mining may be a process of distinguishing and extracting hidden patterns and knowledge from databases and data warehouses. It is also referred to as knowledge Discovery in Databases (KDD) and permits knowledge discovery, data analysis, and data visualization of large databases at a high level of abstraction, while not a selected premise in mind. The operation of data mining is known by employing a technique known as modeling with it to create predictions. There are various algorithms and tools on the market for this purpose. Data mining encompasses a large variety of applications ranging from business to medication to engineering. This paper provides a survey of data mining technology, its models, and task, applications, major problems, and directions for advance analysis of data mining applications. https://sites.google.com/site/ijcsis/vol-11-no-5-may-2013
Sigmod Record, 2002
Mining information from data: A presentday gold rush. Data Mining is a multidisciplinary field which supports knowledge workers who try to extract information in our "data rich, information poor" environment. Its name stems from the idea of mining knowledge from large amounts of data. The tools it provides assist us in the discovery of relevant information through a wide range of data analysis techniques. Any method used to extract patterns from a given data source is considered to be a data mining technique.
Advanced Information and Knowledge Processing, 2005
Knowledge discovery and data mining has recently emerged as an important research direction for extracting useful information from vast repositories of data of various types. This chapter discusses some of the basic concepts and issues involved in this process with special emphasis on different data mining tasks. The major challenges in data mining are mentioned. Finally, the recent trends in data mining are described and an extensive bibliography is provided.
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 ...
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.
ArXiv, 2020
The Symposium on Data Mining and Applications (SDMA 2014) is aimed to gather researchers and application developers from a wide range of data mining related areas such as statistics, computational intelligence, pattern recognition, databases, Big Data Mining and visualization. SDMA is organized by MEGDAM to advance the state of the art in data mining research field and its various real world applications. The symposium will provide opportunities for technical collaboration among data mining and machine learning researchers around the Saudi Arabia, GCC countries and Middle-East region. Acceptance will be based primarily on originality, significance and quality of contribution.
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.
Studies in Classification, Data Analysis, and Knowledge Organization, 2013
The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein.
Data Mining and Knowledge Discovery in Real Life Applications, 2009
This paper addresses the discussion of impact of data mining in today's fast growing world. Data mining means the extraction of hidden predictive information from large databases. It is an interactive information discovery process that includes data acquisition, data integration, data exploration, model building, and model validation. This paper provides an overview of data mining process, some well known techniques, software and few application areas.
2010
It is represented that book articles will be interesting as experts in the field of classifying, data mining and forecasting, and to practical users from medicine, sociology, economy, chemistry, biology, and other areas. General Sponsor: Consortium FOI Bulgaria (www.foibg.com).
In this chapter we discuss the theory and foundational issues in data mining, describe data mining methods and algorithms, and review data mining applications. A section is devoted to summarizing the state of rough sets as related to data mining of real-world databases.
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