Classificat ion approach can also be used for effective means of distinguishing groups or classes of object but it becomes costly so clustering can be use d as preprocessing approach for attribute subset selecti on and classification.Bharati Bharati Mahadev Ramageri PES Modern Institute of Computer Application, Pune Download full-text PDF Read full-text Download full-text PDF Read full-text Download citation Copy link Link copied Read full-text Download citation Copy link Link copied Citations (204) References (2) Figures (1) Abstract and Figures 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.Knowledge discovery Process Figures - available via license: Creative Commons Attribution 4.0 International Content may be subject to copyright.
Berry Linhof Data Mining Techniques File Free Public FullDiscover the worlds research 20 million members 135 million publications 700k research projects Join for free Public Full-text 1 Available via license: CC BY 4.0 Content may be subject to copyright. Abstract 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 orga nizations which have adapted data mining technolo gy to improve their businesses and found excellent results. Keywords: Data mining Techniques; Data mi ning algorithm s; Data mining applications. Overview of Data Mining The development of Information Technology has generated large amount of databases and huge data in various areas. The research in databases and informat ion technology has given rise to an approach to store and manipulate this precious data for further decision making. Data mining is a process of extraction of useful information and patterns from huge data. It is also called as knowledge discovery process, knowledge mining from data, knowledge extraction or data pattern analysis. Figure 1. Knowledge discovery Process Data mining is a logical process that is used to search throug h large amount of data in order to find useful data. The goal of this technique is to find patterns that were previo usly unknown. Once these patterns are found they can further be used to make certain decisions for devel opment of their businesses. Three steps involved are Exploration Pattern identification Deployment Exploration: In the first step of data exploration data is cleaned and transformed into an other form, and important variables and then nature of data based on the problem are determined. Deployment: Patterns are deployed f or desired outcome. Data Mining Algorithms and Techniques Various algorithms and techniques like Classification, Clustering, Regression, Artificial Intelligence, Neural Networks, Association Rules, Decision Trees, Genetic Algorithm, Nearest Neighbor method etc., are used for knowled ge discovery from databases. Fraud detection and credit - risk applications are particularly well suited to this type of analysis. This approach frequently em ploys decision tree or neural network- based classification algorithms. The data classification process involves learning and classification. In Lear ning the training data are analyzed by classification algorithm. In classification test data are used to estimate the accu racy of the classification rules. If the accuracy is acceptable the rules can be applied to the new data tu ples. For a fraud detection a pplication, this would include complete records of both fraudulent and valid activities determined on a record-by-record basis. The classifier-training algorithm uses these pre-classified examples to determine the set of parameters required for proper discrimination. The algorithm th en encodes these parameters into a model called a classifier. Types of classification models: Classification by decision tree inducti on Bayesian Classification Neural Networks Support Vector Machines (SVM) Classification Based on Associations 2.2. Clustering Clustering can be said as identification of similar cla sses of objects. By using cl ustering techniques we can further identify dense and sparse re gions in object space and can discove r overall distribution pattern and correlations among data attributes.
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