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Data mining refers to extracting or mining knowledge from large amountsof data. The term is actually a misnomer. Thus, data miningshould have been more appropriately named as knowledge mining which emphasis on mining from large amounts of data.
Encyclopedia of Artificial Intelligence
Data mining is the process of extracting previously unknown information from large databases or data warehouses and using it to make crucial business decisions. Data mining tools find patterns in the data and infer rules from them. The extracted information can be used to form a prediction or classification model, identify relations between database records, or provide a summary of the databases being mined. Those patterns and rules can be used to guide decision making and forecast the effect of those decisions, and data mining can speed analysis by focusing attention on the most important variables.
Data mining is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data
IJRCAR, 2015
Data mining has assumed a global proportion in every sector such as higher education, science, biodiversity information technology, mathematics geology and many more…Data mining provides a practical means for classification and distinction of tremendous amount of data related to any field of information .data mining is more of exploratory analysis. It basically aims at expanding the learning culture and helps in predicting the future need of that data.
Kalpa Publications in Engineering
Information mining is known as the extraction of concealed prescient data from expansive databases whose primary concentration is to enable organizations to concentrate on the most critical data to display in their information stockrooms. Data mining can in like manner be called as the examination of data and the use of the distinctive programming techniques for finding illustrations and regularities in the given courses of action of data. The articulation "Electronic exchange" (or web business) ordinarily implies the usage of an electronic medium to finish all the business trades. Numerous a times it alludes to the offer of items by means of Internet, however it likewise incorporates the acquiring of systems through Internet. This primary concentration of this paper is to give the fundamental presentation about the different information mining methods accessible and furthermore to break down these procedures based on their execution. The paper additionally characterizes t...
Encyclopedia of Information Science and Technology, Third Edition
2014
Data mining is a process of using different algorithms to find useful patterns or models from data. It is a process of selecting, exploring and modeling large amount of data. Mostly used in technology and also used in different areas of real life like finance, marketing, business. It can also be used in different social science methodologies, such as psychology, cognitive science and human behavior etc. The ability to continually change and acquire new understanding is a driving force for the application of DM. This allows many new future applications of data mining (1) as in today's world the need of data in every field is growing very vast. So, for satisfying our need there should be proper Data Mining techniques available. In this paper we are presenting valuable information about data mining.
International Journal of Recent Scientific Research, 2018
Data mining is the process of discovering the knowledge by analysing and extracting the data from various highly repositories and the result bases on the useful and functional information's for the user. By the passage of time data mining is growing very vastly and became the famous technology by analysing and extraction of knowledge. We are standing at the point where life can have a better understanding of the problems. There was a time to start an active research on data mining but the limitation of this technology is under predictions as; is this technology has any limits for the future or it is limitless towards the growing world? Why only data mining technology is involves in the refining process of data? How efficiently the future relies on this technology? Is this new technology is so capable of being popular and more powerful in all respective fields? What are its limitations and how it is dominating the future? In this paper we have revealed the facts of growing fields with this manifesto and how it is affecting anonymously and how reliable the future is on this technology? And why this technology is so important for future? et.al, 1996 5).(Rygielski et.al, 2002) narrates as "Data mining is a set of methods used in the knowledge discovery process to distinguish previously unknown relationships and patterns within the data" 6 .The most dynamic definition of data mining according to the (Prabakaran et.al, 2018 7), "data mining is the process of Decision Tree, Neural Networks, Rule Inductions, Neatest Neighbours and Genetic Algorithms".
2011
In this paper we considered several frameworks for data mining. These frameworks are based on different approaches, including inductive databases approach, the reductionist statistical approaches, data compression approach, constructive induction approach and some others. We considered advantages and limitations of these frameworks. We presented the view on data mining research as continuous and never- ending development process of an adaptive DM system towards the efficient utilization of available DM techniques for solving a current problem impacted by the dynamically changing environment. We discussed one of the traditional information systems frameworks and, drawing the analogy to this framework, we considered a data mining system as the special kind of adaptive information system. We adapted the information systems development framework for the context of data-mining systems development.
Over the past years data warehousing and data mining tools have evolved from research into a unique and popular business application class for decision support and business intelligence. This paper focuses on presenting the applications of data mining in the business environment. It contains a general overview of data mining, providing a definition of the concept, enumerating six primary data mining techniques and mentioning the main fields for which data mining can be applied. The paper also presents the main business areas which can benefit from the use of data mining tools, along with their use cases: retail, banking and insurance. Also the main commercially available data mining tools and their key features are presented within the paper. Theoretical and empirical literature was reviewed and various gaps in literature were identified. Besides the analysis of data mining and the business areas that can successfully apply it, the paper suggested and concluded that firms and scholars need to carry out more empirical research in the area of integrity of data mining and data warehousing since this will help eliminate marketing errors in operations and practice. Over the past decade, data mining became a matter of considerable importance due to the large amounts of data available in the applications belonging to various domains. Data mining is not static but a fast-expanding field, which applies advanced data analysis techniques, from statistics, machine learning, database systems or artificial intelligence, in order to discover relevant patterns, trends and relations contained within the data, information impossible to observe using other techniques . The concept of Data Mining and Data Warehousing is gaining increasingly emphasis as a business information management tool that is expected to disclose knowledge structures that can guide decisions in conditions of limited certainty. A data warehouse guides business analysis and decision-making by creating an enterprise-wide integrated database of summarized, historical information. It integrates data from multiple, incompatible sources. By transforming data into meaningful information, and a data warehouse allows the manager to perform more substantive, accurate and consistent analysis (Agbonifoh, et al., 2006;.
2012 Federated Conference on Computer Science and Information Systems (FedCSIS), 2012
Data mining is recognized as an important field where one has the possibility to become accustomed both with analysis techniques and methods and with a state of mind. By means of data mining it is possible to develop critical skills that are essential in today's information technology. We present our experience in teaching a data mining module, within an Information System course, centered around a few key aspects: a convergence of theoretical Information Systems aspects and computing skills through programming a complete data mining analysis in Matlab; a project centered learning experience; a sharing of resources that are commented on both by the teacher and by peers facilitating the flow of information and the development of critical skills; a guided inquiry process where the students, when needed, are guided through appropriate questions in the right direction; and finally special attention to requiring motivation of each decision and step undertaken. As a case study we pres...
2018
Data mining (the analysis step of the "Knowledge Discovery in Databases" process, or KDD) an interdisciplinary subfield of computer science, is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. 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. A warehouse is a commercial building for storage of goods. It
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022
Datamining is a method of finding interested patterns from huge volume of data. Datamining techniques helps to make business decisions. It analyses information from multiple sources like DataMart, databases. In this paper, we are focussing on datamining tasks and its variety of applications in different fields, which is boon to the society.
Data Mining and Knowledge Discovery in Real Life Applications, 2009
International Journal on Recent and Innovation Trends in Computing and Communication
Data mining is the process of identification of the patterns and trends in large and complex data volumes. The normal data processing will not be helpful in handling such complex data. Hence data mining is used to handle such large volumes of the data. This paper will explain the steps of data mining process, types of data mining. It will also explain the importance and advantages of the data mining and challenges involved in the implementation of the data mining.
— 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.
ArXiv, 2008
Since many years, theoretical concepts of Data Mining have been developed and improved. Data Mining has become applied to many academic and industrial situations, and recently, soundings of public opinion about privacy have been carried out. However, a consistent and standardized definition is still missing, and the initial explanation given by Frawley et al. has pragmatically often changed over the years. Furthermore, alternative terms like Knowledge Discovery have been conjured and forged, and a necessity of a Data Warehouse has been endeavoured to persuade the users. In this work, we pick up current definitions and introduce an unified definition that covers existing attempted explanations. For this, we appeal to the natural original of chemical states of aggregation.
—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.
Challenges of the Knowledge Society, 2011
Managers of economic organizations have at their disposal a large volume of information and practically facing an avalanche of information, but they can not operate studying reports containing detailed data volumes without a correlation because of the good an organization may be decided in fractions of time. Thus, to take the best and effective decisions in real time, managers need to have the correct information is presented quickly, in a synthetic way, but relevant to allow for predictions and analysis.This paper wants to highlight the solutions to extract knowledge from data, namely data mining. With this technology not only has to verify some hypotheses, but aims at discovering new knowledge, so that economic organization to cope with fierce competition in the market.
Opportunities, Limitations and Risks, 2004
In today's business world, the use of computers for everyday business processes and data recording has become virtually ubiquitous. With the advent of this electronic age comes one priceless by-product-data. As more and more executives are discovering each day, companies can harness data to gain valuable insights into their customer base. Data mining is the process used to take these immense streams of data and reduce them to useful knowledge. Data mining has limitless applications, including sales and marketing, customer support, knowledge-base development, not to mention fraud detection for virtually any field, etc. "Data mining," a bit of a misnomer, refers to mining the data to find the gems hidden inside the data, and as such it is the most often-used reference to this process. It is important to note, however, that data mining is only one part of the Knowledge Discovery in Databases process, albeit it is the workhorse. In this chapter, we provide a concise description of the Knowledge Discovery process,
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