Abstract
With the increasing of database applications, mining interestinginformation from huge databases becomes of most concern and avariety of mining algorithms have been proposed in recent years.As we know, the data processed in data mining may be obtainedfrom many sources in which different data types may be used.However, no algorithm can be applied to all applications due tothe difficulty for fitting data types of the algorithm, so the selectionof an appropriate mining algorithm is based on not only thegoal of application, but als ....etc

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developing an algorithm for data mining using clustering genetic algorithm at c++



With an enormous amount of data stored in databases and data warehouses, it is increasingly important to develop powerful tools for analysis of such data and mining interesting knowledge from it. Data mining is more than slicing and dicing of data; it is a powerful business intelligence technology that provides knowledge about unknown patterns and events from large databases. Data mining technologies have known to significantly reduce operational costs and ....etc

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ODAM: An Optimized Distributed Association Rule Mining Algorithm
Abstract: Association rule mining is an active data mining research area. However, most ARM algorithms cater to a centralized environment. In contrast to previous ARM algorithms, ODAM is a distributed algorithm for geographically distributed data sets that reduces communication costs. Modern organizations are geographically distributed. Typically, each site locally stores its ever-increasing amount of day-to-day data. Using centralized data mining to discover useful patterns in s ....etc

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Classification tree models are simple and effective as software quality prediction models, and timely predictions of defects from such models can be used to achieve high software reliability.To evaluate the performance of the the suitable algorithms is applied to calculate the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Receiver Operating Characteristic (ROC), Accuracy measure is are also applied. the above problem is implemented using Weka dataming tool ....etc

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An Efficient Algorithm for Mining Frequent Patterns ....etc

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Abstract

Prediction of an event at a time series is quite important for engineering and economy problems. Time series data mining combines the fields of time series analysis and data mining techniques. This method creates a set of methods that reveal hidden temporal patterns that are characteristic and predictive of time series events. Time series data mining examines the time series in a phase space. In this paper, a prediction algorithm using time series data mining based on fuzzy logic is proposed. Earthquake prediction has been ....etc

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Abstract
The association rules represent an important class of knowledge that can be discovered from data warehouses. Current research efforts are focused on inventing efficient ways of discovering these rules from large databases. As databases grow, the discovered rules need to be verified and new rules need to be added to the knowledge base. Since mining afresh every time the database grows is inefficient, algorithms for incremental mining are being investigated. Their primary aim is to avoid or minimize scans of the older database ....etc

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A Web Usage Mining Framework for Mining Evolving User Profiles in Dynamic Web Site-IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 20, NO. 2,

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Abstract—I
n this paper, we present a complete framework and findings in mining Web usage patterns from Web log files of a real Web site that has all the challenging aspects of real-life Web usage mining, including evolving user profiles and external data describing an ontology of the Web content. Even though the Web site under study is part of a nonprofit organization th ....etc

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An Efficient Algorithm for Mining Frequent Patterns
Abstract:
Mining frequent patterns are one of the most important research topics in data mining. The function is to mine the transactional data which describes the behavior of the transaction. In an online business or in an online shopping the customers can purchase items together. Frequent patterns are patterns such as item sets, subsequences or substructures that appear in a data set frequently. Many efficient algorithms were developed based on the data structure a ....etc

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