scispace - formally typeset
Open AccessProceedings Article

MineSet: an integrated system for data mining

Cliff Brunk, +2 more
- pp 135-138
Reads0
Chats0
TLDR
MineSet supports the knowledge discovery process from data access and preparation through iterative analysis and visualization to deployment, and third party vendors can interface to the MineSet tools for model deployment and for integration with other packages.
Abstract
MineSet™, Silicon Graphics' interactive system for data mining, integrates three powerful technologies: database access, analytical data mining, and data visualization. It supports the knowledge discovery process from data access and preparation through iterative analysis and visualization to deployment. Mine-Set is based on a client-server architecture that scales to large databases. The database access component provides a rich set of operators that can be used to preprocess and transform the stored data into forms appropriate for visualization and analytical mining. The 3D visualization capabilities allow direct data visualization for exploratory analysis, including tools for displaying high-dimensional data containing geographical and hierarchical information. The analytical mining algorithms help identify potentially interesting models of the data, which can be viewed using visualization tools specialized for the learned models. Third party vendors can interface to the MineSet tools for model deployment and for integration with other packages.

read more

Content maybe subject to copyright    Report

Citations
More filters
Journal ArticleDOI

Machine learning

TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Journal ArticleDOI

From visual data exploration to visual data mining: a survey

TL;DR: This work surveys work on the different uses of graphical mapping and interaction techniques for visual data mining of large data sets represented as table data and reviews recent innovative approaches that attempt to integrate visualization into the DM/KDD process, using it to enhance user interaction and comprehension.
Proceedings ArticleDOI

Discovering Web access patterns and trends by applying OLAP and data mining technology on Web logs

TL;DR: The design of WebLogMiner is presented, current progress is reported and future work in this direction is outlined, which can improve the system performance, enhance the quality and delivery of Internet information services to the end user, and identify populations of potential customers for electronic commerce.
Patent

Apparatus and accompanying methods for visualizing clusters of data and hierarchical cluster classifications

TL;DR: In this article, the authors present an interactive graphical user interface for visualizing clusters (categories) and segments (summarized clusters) of data, which automatically categorizes incoming case data into clusters, summarizes those clusters into segments, determines similarity measures for the segments, scores the selected segments through the similarity measures, and then forms and visually depicts hierarchical organizations of those selected clusters.
Journal ArticleDOI

A survey of data mining and knowledge discovery software tools

TL;DR: An overview of common knowledge discovery tasks and approaches to solve these tasks is provided, and a feature classification scheme that can be used to study knowledge and data mining software is proposed.
References
More filters
Book

C4.5: Programs for Machine Learning

TL;DR: A complete guide to the C4.5 system as implemented in C for the UNIX environment, which starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting.
Book

Pattern classification and scene analysis

TL;DR: In this article, a unified, comprehensive and up-to-date treatment of both statistical and descriptive methods for pattern recognition is provided, including Bayesian decision theory, supervised and unsupervised learning, nonparametric techniques, discriminant analysis, clustering, preprosessing of pictorial data, spatial filtering, shape description techniques, perspective transformations, projective invariants, linguistic procedures, and artificial intelligence techniques for scene analysis.
Journal ArticleDOI

Machine learning

TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Journal ArticleDOI

Wrappers for feature subset selection

TL;DR: The wrapper method searches for an optimal feature subset tailored to a particular algorithm and a domain and compares the wrapper approach to induction without feature subset selection and to Relief, a filter approach tofeature subset selection.