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There were early methods which were used to identify data mining there are mainly two: regression analysis and bayes theorem.
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The method of data mining has been there for so many centuries and it is used up to date. One must know data mining definition so that he can be in a position to make data. This definition can come in different terms data snooping, data fishing and data dredging all this refer to data mining but it depends in which department one is. It is commonly used in marketing, scientific information and research work, fraud detection and surveillance and many more and most of this work is done using a computer. Data transforms is processed into information and it is mostly used in different ways depending on what information one is extracting and from where the person is extracting the information. It has become very important now days because data that is processed is usually kept for future reference and mainly for security purposes in a company.
#FMINER XPATH SOFTWARE#
There is also free software and shareware such as INTEXT, S-EM (Spy-EM), and Vivisimo/Clusty.ĭata Mining - Retrieving Information From Dataĭata mining definition is the process of retrieving information from data. Some popular data mining software includes: Connexor Machines, Copernic Summarizer, Corpora, DocMINER, DolphinSearch, dtSearch, DS Dataset, Enkata, Entrieva, Files Search Assistant, FreeText Software Technologies, Intellexer, Insightful InFact, Inxight, ISYS:desktop, Klarity (part of Intology tools), Leximancer, Lextek Onix Toolkit, Lextek Profiling Engine, Megaputer Text Analyst, Monarch, Recommind MindServer, SAS Text Miner, SPSS LexiQuest, SPSS Text Mining for Clementine, Temis-Group, TeSSI®, Textalyser, TextPipe Pro, TextQuest, Readware, Quenza, VantagePoint, VisualText(TM), by TextAI, Wordstat. Some of the most popular data mining tools are: decision trees, information gain, probability, probability density functions, Gaussians, maximum likelihood estimation, Gaussian Baves classification, cross-validation, neural networks, instance-based learning /case-based/ memory-based/non-parametric, regression algorithms, Bayesian networks, Gaussian mixture models, K-Means and hierarchical clustering, Markov models, support vector machines, game tree search and alpha-beta search algorithms, game theory, artificial intelligence, A-star heuristic search, HillClimbing, simulated annealing and genetic algorithms. The different kinds of data are: text mining, web mining, social networks data mining, relational databases, pictorial data mining, audio data mining and video data mining. Some of the main applications of data mining are in direct marketing, e-commerce, customer relationship management, healthcare, the oil and gas industry, scientific tests, genetics, telecommunications, financial services and utilities.
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Some advanced data mining tools can perform database integration, automated model scoring, exporting models to other applications, business templates, incorporating financial information, computing target columns, and more. However, the use of some advanced technologies makes it a decision making tool as well. It is basically an extension of some statistical methods like regression. It is being used increasingly in business applications for understanding and then predicting valuable information, like customer buying behavior and buying trends, profiles of customers, industry analysis, etc. The main kinds of data mining software are: clustering and segmentation software, statistical analysis software, text analysis, mining and information retrieval software and visualization software.ĭata mining is gaining a lot of importance because of its vast applicability. Data mining is thus also known as Knowledge Discovery in Databases (KDD) since it involves searching for implicit information in large databases. It is basically a technical and mathematical process that involves the use of software and specially designed programs.
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Data mining helps to extract useful information from great masses of data, which can be used for making practical interpretations for business decision-making. Data mining is the retrieving of hidden information from data using algorithms.