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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.
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.
Data mining automates the process of finding predictive information in large databases. Questions that traditionally required extensive handson analysis can now be answered directly from the dataquickly. A typical example of a predictive problem is targeted marketing. Data mining uses data on past promotional mailings to identify the targets most likely to maximize return on investment in future mailings. Other predictive problems include forecasting bankruptcy and other forms of default, and identifying segments of a population likely to respond similarly to given events.
This tutorial provides an overview of the data mining process. The tutorial also provides a basic understanding of how to plan, evaluate and successfully refine a data mining project, particularly in terms of model building and model evaluation. Methodological considerations are discussed and illustrated. After explaining the nature of data mining and its importance in business, the tutorial describes the underlying machine learning and statistical techniques involved. It describes the CRISP-DM standard now being used in industry as the standard for a technology-neutral data mining process model. The paper concludes with a major illustration of the data mining process methodology and the unsolved problems that offer opportunities for research. The approach is both practical and conceptually sound in order to be useful to both academics and practitioners.
Data mining is a concept which looks for valuable resources as exapmles from substantial measure of information i.e from a large amount of data. The thoery or the information in this paper examines few of the data mining procedures, calculations and a portion of the associations which have adjusted information mining innovation to enhance their organizations and discovered great outcomes.
Data Mining and Knowledge Discovery in Real Life Applications, 2009
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
Data mining, the extraction of hidden predictive information from large databases, invented as a powerful new technology with great potential to help companies and organizations to focus on the most important information in their data warehouses. Data mining uses machine learning, statistical and visualization techniques to discovery and present knowledge in a form which is easily comprehensible to humans. Various popular data mining tools and techniques are available today for supporting large amount of applications. Data mining tools predict future trends and behaviors, allowing it’s users to make proactive, knowledge-driven decisions. They scour databases for hidden patterns, finding predictive information that experts may miss because it lies outside their expectations. With these large amount of features and applications there are some challenging issues also which are not exclusive and are not ordered in any way. This paper presents an overview of the data mining technique, Some of its vital applications and issues needs to be addressed.
—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 is a process which is used by companies to turn raw data into useful information. By using software to look for patterns in large batches of data, businesses can learn more about their customers and develop more effective marketing strategies as well as increase sales and decrease costs. It depends on constructive data collection and warehousing as well as computer processing. Data mining used to analyze patterns and relationships in data based on what users request. For example, data mining software can be used to create classes of information. When companies centralize their data into one database or program, it is known as data warehousing. Accompanied a data warehouse, an organization may spin off segments of the data for particular users and utilize. While, in other cases, analysts may begin with the type of data they want and create a data warehouse based on those specs. Regardless of how businesses and other entities systemize their data, they use it to support management's decision-making processes.
Data mining has become a popular buzzword but, in fact, promises to revolutionize commercial and scientific exploration. Databases range from millions to trillions of bytes of data. Data mining, the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. The automated, prospective analyses offered by data mining move beyond the analyses of past events provided by retrospective tools typical of decision support systems. Data mining tools can answer business questions that traditionally were too time consuming to resolve.
Information by itself is no longer perceived as an asset. Billions of business transactions are recorded in enterprise scale data warehouses every day. Acquisition, storage and management of business information are commonplace and often automated. Recent advances in (remote or other) sensor technologies have led to the development of scientific data repositories.
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 ...
The Knowledge Engineering Review, 2011
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. Unlike other innovations in AI and KE, data mining can be argued to be an application rather then a technology and thus can be expected to remain topical for the foreseeable future. This paper presents a brief review of the history of data mining, up to the present day, and some insights into future directions.
Encyclopedia of Knowledge Management, 2006
—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.
Data warehousing and mining : concepts, methodologies, tools and applications / John Wang, editor.
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