NDLI: Visualizing Distributions and Classification Accuracy

Content Provider	IEEE Xplore Digital Library
Author	Groth, D.P.
Copyright Year	2006
Description	Author affiliation: Indiana Univ. Sch. of Informatics, Bloomington, IN (Groth, D.P.)
Abstract	Data mining is the search for novel, actionable information within data. It is important to note that the number of records in the data being analyzed is only one (and perhaps a small) factor in determining the complexity of a given data mining technique. Most complexity in data mining arises from the distribution of values contained in the data - not the number of records. In this paper, we utilize straightforward histogram-based visualizations to gain insight into how the performance of a well-studied data mining technique, the naive-Bayes classifier, performs under various discretization schemes for both continuous and discrete values. The resulting visualization system provides users with a tool that describes the underlying model of the data used by the classifier. Exploratory visualizations of the distributions of training data can be selected based on expert domain knowledge and then combined to apply to the test data
Starting Page	389
Ending Page	394
File Size	308893
Page Count	6
File Format	PDF
ISBN	0769526020
ISSN	15506037
DOI	10.1109/IV.2006.129
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-07-05
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data visualization Data mining Informatics Data analysis Performance gain Training data Testing Association rules Cleaning Remuneration
Content Type	Text
Resource Type	Article

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