Data Mining

Paper Summary:

This study examines the use of advanced techniques of data clustering in algorithms that employ abstract categories for the pattern matching and pattern recognition procedures used in data mining searches of web documents. The paper discusses the significance and purpose of the study. The author gives exact methodology, organization and statistical results of the study. It also evaluates implementation of data clustering for web based searches and its feasibility.

Table of Contents
Background of the Problem
Significance of the Study
Definitions
Overview of the Methodology
Organization of the Study
Purpose of the Study
Background and a Review of Literature
Alternative Solutions
Feasibility Tests
Evaluation and Implementation
References

From the Paper:

"Documents are commonly represented as vector space models. In this model, each document is represented by a point in the space that roughly corresponds with the union of the primary words in the document. The process includes filtering out common words (such as pronouns and conjunctions), ignoring words that are unique to the document, and words are stemmed in order to reduce them to canonical form. Once this is done , the document can be expressed in the form of vectors, and then those vectors can be used to plot a pint in virtual space that represents that specific document. The words of the document are also weighted; without this provision, the more commonly a word appears in a document, the more important it is considered (which is often not really the case). Conversely, words that appear infrequently in a document are considered discriminatory- they serve as a distinguishing feature for the document in question.[10]"

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