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Data Mining
Data Mining - DM
Publications: 32,500| Citation Count: 258,270
Stemming Variations: data mined, Data Mines, Data Mine, Data Minings
Cumulative Annual
    • Data mining (DM) modeling is a process of transforming information enfolded in a dataset into a form amenable to human cognition...

    Yan Liu, et al. Interactive Visual Decision Tree Classification

    • Data Mining(DM) is the process of extracting implicit, valuable, and interesting information from large sets of data. As huge amounts of data have been stored in traffic and transportation databases, data warehouses, geographic information systems, and other information repositories, data mining is receiving substantial interest from both academia and industry...

    Shashi Shekhar, et al. Data Mining and Visualization of Twin-Cities Traffic Data

    • Data Mining (DM) is a very crucial issue in knowledge discovery processes. The basic facilities to create data mining models were implemented successfully on Oracle 9i as the extension of the database server. DM tools enable developers to create Business Intelligence (BI) applications. As a result Data Mining models can be used as support of knowledge-based management. The main goal of the paper is to present new features of the Oracle platform in building and testing DM models...

    Krzysztof Hauke, et al. Building Data Mining Models in the Oracle 9i Environment

    • Data mining is an area of data analysis that has arisen in response to new data analysis challenges, such as those posed by massive data sets or non-traditional types of data. Association analysis, which seeks to find patterns that describe the relationships of attributes (variables) in a binary data set, is an area of data mining that has created a unique set of data analysis tools and concepts that have been widely employed in business and science...

    Michael Steinbach, et al. Objective Measures for Association Pattern Analysis

    • Data mining is an interdisciplinary field, having applications in diverse areas like bioinformatics, medical informatics, scientific data analysis, financial analysis, consumer profiling, etc. In each of these application domains, the amount of data available for analysis has exploded in recent years, making the scalability of data mining implementations a critical factor. To this end, parallel versions of most of the well-known data mining techniques have been developed in recent years...

    Ruoming Jin, et al. A Middleware for Developing Parallel Data Mining Applications

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