| An Efficient Association Rule Mining Algorithm |
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| In this paper, we propose a new Association Rule Mining algorithm for Classification (ARMC). Our algorithm extracts the set of rules, specific to each class, using a fuzzy approach to select the items and does not require the user to provide thresholds. |
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| Practial Applications of DataMining |
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| Topics Covered: Mining Object, Spatial, Multimedia, Text, and Web Data, Aggregation and Approximation in Spatial and Multimedia Data Generalization, Generalization of Object Identifiers and Class/Subclass Hierarchies, Generalization of Class Composition Hierarchies, Construction and Mining of Object Cubes etc. |
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| Outlier Analysis |
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| One problem in large-scale analysis of this sort is that erroneous measurements may slip by, due to insufficient attention. Much of the phonetic variation to documented herein is quite extreme, much more extreme than what is found in monitored ``laboratory'' speech. |
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| OLAP |
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| OLAP (Online Analytical Processing) is a methodology to provide end users with access to large amounts of data in an intuitive and rapid manner to assist with deductions based on investigative reasoning. |
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| Multiobjective Association Rule Mining |
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| This paper discusses the application of evolutionary multiobjective optimization (EMO) to association rule mining. We focus our attention especially on classification rule mining where the consequent part of each rule is a class label. First we briefly explain evolutionary multiobjective classification rule mining techniques. |
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| Data Integration For Data Warehouse |
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| Data warehousing embraces technology of integrating data from multiple distributed data sources and using that data in annotated and aggregated form to support business decision-making and enterprise management. |
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| DetectingOutliers |
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| Topics Covered: Detecting univariate outliers, Detecting multivariate outliers |
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| Data Preprocessing |
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| Topics Covered: Why preprocessing, Data cleaning, Data transformation, Data reduction, Discretization and generating concept hierarchies |
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| Data Mining - Basics |
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| Generally, data mining (sometimes called data or knowledge discovery) 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. |
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| Data Mining |
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| Data mining is a powerful new technology with great potential to help companies focus on the most important information in the data they have collected about the behavior of their customers and potential customers. It discovers information within the data that queries and reports can't effectively reveal. |
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| Data Extraction |
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| Data Extraction, Transformation, and Migration Tools |
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| Model-Based Cluster Analysis |
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| We consider the problem of determining the structure of clustered data, without prior knowledge of the number of clusters or any other information about their composition. Data are represented by a mixture model in which each component corresponds to a different cluster. |
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| Bayesian Classification Theory |
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| The task of inferring a set of classes and class descriptions most likely to explain a given data set can be placed on a firm theoretical foundation using Bayesian statistics. Within this framework, and using various mathematical and algorithmic approximations, the Auto Class system searches for the most probable classifications, automatically choosing the number of classes and complexity of class descriptions. |
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