Integration of Clustering and Rule Induction Mining Framework for Evaluation of Web Usage Knowledge Discovery System

The overall size of software packages has grown considerably over recent years. Modular programming, object-oriented design and the use of static and dynamic libraries have all contributed towards the reusability and maintainability of these packages. One of the latest methodologies that aims to further improve software design is the use of component-based services. The Component Object Model (COM) is a specification that provides a standard for writing software components that are easily interoperable. The most common platform for component libraries is on Microsoft Windows, where COM objects are an integral part of the operating system and used extensively in most major applications. This chapter examines the use of COM in the design of search engines for knowledge discovery and data mining using modern heuristic techniques and how adopting this approach benefits the design of a commercial toolkit. The chapter describes how search engines have been implemented as COM objects and how representation and problem components have been created to solve rule induction problems in data mining.

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Knowledge Discovery from Web Usage Data: Extraction of Sequential Patterns through ART1 Neural Network Based Clustering Algorithm

International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007) ◽

10.1109/iccima.2007.289 ◽

2007 ◽

Cited By ~ 1

Author(s):

G.T. Raju ◽

P. S. Satyanarayana

Keyword(s):

Neural Network ◽

Knowledge Discovery ◽

Clustering Algorithm ◽

Data Extraction ◽

Sequential Patterns ◽

Web Usage ◽

Usage Data

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VISUALIZATION SUPPORT FOR USER-CENTERED MODEL SELECTION IN KNOWLEDGE DISCOVERY AND DATA MINING

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213001000726 ◽

2001 ◽

Vol 10 (04) ◽

pp. 691-713 ◽

Cited By ~ 8

Author(s):

TUBAO HO ◽

TRONGDUNG NGUYEN ◽

DUCDUNG NGUYEN ◽

SAORI KAWASAKI

Keyword(s):

Data Mining ◽

Model Selection ◽

Knowledge Discovery ◽

Performance Metrics ◽

Model Performance ◽

Discovery Process ◽

Whole Process ◽

Discovery System ◽

Competing Models ◽

Visualization Techniques

The problem of model selection in knowledge discovery and data mining—the selection of appropriate discovered patterns/models or algorithms to achieve such patterns/models—is generally a difficult task for the user as it requires meta-knowledge on algorithms/models and model performance metrics. Viewing knowledge discovery as a human-centered process that requires an effective collaboration between the user and the discovery system, our work aims to make model selection in knowledge discovery easier and more effective. For such a collaboration, our solution is to give the user the ability to try easily various alternatives and to compare competing models quantitatively and qualitatively. The basic idea of our solution is to integrate data and knowledge visualization with the knowledge discovery process in order to the support the participation of the user. We introduce the knowledge discovery system D2MS in which several visualization techniques of data and knowledge are developed and integrated into the steps of the knowledge discovery process. The visualizers in D2MS greatly help the user gain better insight in each step of the knowledge discovery process as well the relationship between data and discovered knowledge in the whole process.

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