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Data mining

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Full-Text Articles in Computer Sciences

Data Warehouse Applications In Modern Day Business, Carla Mounir Issa Jan 2002

Data Warehouse Applications In Modern Day Business, Carla Mounir Issa

Theses Digitization Project

Data warehousing provides organizations with strategic tools to achieve the competitive advantage that organazations are constantly seeking. The use of tools such as data mining, indexing and summaries enables management to retrieve information and perform thorough analysis, planning and forcasting to meet the changes in the market environment. in addition, The data warehouse is providing security measures that, if properly implemented and planned, are helping organizations ensure that their data quality and validity remain intact.

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Studying The Functional Genomics Of Stress Responses In Loblolly Pine With The Expresso Microarray Experiment Management System, Lenwood S. Heath, Naren Ramakrishnan, Ronald R. Sederoff, Ross W. Whetten, Boris I. Chevone, Craig Struble, Vincent Y. Jouenne, Dawei Chen, Leonel Van Zyl, Ruth Grene Jan 2002

Studying The Functional Genomics Of Stress Responses In Loblolly Pine With The Expresso Microarray Experiment Management System, Lenwood S. Heath, Naren Ramakrishnan, Ronald R. Sederoff, Ross W. Whetten, Boris I. Chevone, Craig Struble, Vincent Y. Jouenne, Dawei Chen, Leonel Van Zyl, Ruth Grene

Mathematics, Statistics and Computer Science Faculty Research and Publications

Conception, design, and implementation of cDNA microarray experiments present a variety of bioinformatics challenges for biologists and computational scientists. The multiple stages of data acquisition and analysis have motivated the design of Expresso, a system for microarray experiment management. Salient aspects of Expresso include support for clone replication and randomized placement; automatic gridding, extraction of expression data from each spot, and quality monitoring; flexible methods of combining data from individual spots into information about clones and functional categories; and the use of inductive logic programming for higher-level data analysis and mining. The development of Expresso is occurring in parallel with …

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A Review Of Data Mining Techniques, Sang Jun Lee, Keng Siau Oct 2001

A Review Of Data Mining Techniques, Sang Jun Lee, Keng Siau

Research Collection School Of Computing and Information Systems

Terabytes of data are generated everyday in many organizations. To extract hidden predictive information from large volumes of data, data mining (DM) techniques are needed. Organizations are starting to realize the importance of data mining in their strategic planning and successful application of DM techniques can be an enormous payoff for the organizations. This paper discusses the requirements and challenges of DM, and describes major DM techniques such as statistics, artificial intelligence, decision tree approach, genetic algorithm, and visualization.

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Predictive Self-Organizing Networks For Text Categorization, Ah-Hwee Tan Apr 2001

Predictive Self-Organizing Networks For Text Categorization, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

This paper introduces a class of predictive self-organizing neural networks known as Adaptive Resonance Associative Map (ARAM) for classification of free-text documents. Whereas most sta- tistical approaches to text categorization derive classification knowledge based on training examples alone, ARAM performs supervised learn- ing and integrates user-defined classification knowledge in the form of IF-THEN rules. Through our experiments on the Reuters-21578 news database, we showed that ARAM performed reasonably well in mining categorization knowledge from sparse and high dimensional document feature space. In addition, ARAM predictive accuracy and learning efficiency can be improved by incorporating a set of rules derived from …

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Knowledge Discovery As An Aid To Organizational Creativity, Keng Siau Dec 2000

Knowledge Discovery As An Aid To Organizational Creativity, Keng Siau

Research Collection School Of Computing and Information Systems

Computers can play an important role in the creative process. With the abundance of data and increasing speed of computers, creativity can now be stimulated and enhanced with knowledge mined from available data. The process is known as knowledge discovery or data mining. Knowledge discovery is the process of discovering interesting associations among data in the database. Users in the creativity process can feed on the discovered associations to generate creative solutions. The objective of this paper is to present knowledge discovery as an aid to creativity. The paper first presents the concept of knowledge discovery and then discusses the …

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Knowledge Discovery In Biological Databases : A Neural Network Approach, Qicheng Ma Aug 2000

Knowledge Discovery In Biological Databases : A Neural Network Approach, Qicheng Ma

Dissertations

Knowledge discovery, in databases, also known as data mining, is aimed to find significant information from a set of data. The knowledge to be mined from the dataset may refer to patterns, association rules, classification and clustering rules, and so forth. In this dissertation, we present a neural network approach to finding knowledge in biological databases. Specifically, we propose new methods to process biological sequences in two case studies: the classification of protein sequences and the prediction of E. Coli promoters in DNA sequences. Our proposed methods, based oil neural network architectures combine techniques ranging from Bayesian inference, coding theory, …

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Clouds: A Decision Tree Classifier For Large Datasets, Khaled Alsabti, Sanjay Ranka, Vineet Singh Jan 1998

Clouds: A Decision Tree Classifier For Large Datasets, Khaled Alsabti, Sanjay Ranka, Vineet Singh

Electrical Engineering and Computer Science - All Scholarship

Classification for very large datasets has many practical applications in data mining. Techniques such as discretization and dataset sampling can be used to scale up decision tree classifiers to large datasets. Unfortunately, both of these techniques can cause a significant loss in accuracy. We present a novel decision tree classifier called CLOUDS, which samples the splitting points for numeric attributes followed by an estimation step to narrow the search space of the best split. CLOUDS reduces computation and I/O complexity substantially compared to state of the art classifiers, while maintaining the quality of the generated trees in terms of accuracy …

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