Application of Rough Sets in Data Mining
A Project Report
Submitted in partial fulfilment of
the requirements for the award of the degree of
Master of Technology
in
Computer Science and Engineering
by
Abdul Nassar . A.A.
M105101
Department of Computer Science & Engineering
College of Engineering Trivandrum
Ker ....etc

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A Framework for Mining Sequential Patterns from Spatio-Temporal Event Data Sets-IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 20, NO. 4,-java

Abstract—+
Given a large spatio-temporal database of events, where each event consists of the fields event ID, time, location, and event type, mining spatio-temporal sequential patterns identifies significant event-type sequences. Such spatio-temporal sequential patterns are crucial to the investigation of spatial and temporal evolutions of phenomena in many application domains. R ....etc

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Sets and Venn Diagrams



What is a Venn Diagram?


Everything inside the black outline of the rectangle is defined as the “universal set” or U.

Set U is the main set and then there are subsets.
The blue circle is one subset and the purple is another subset of U.


The Diagram


Each circle is a set representing its own collection of elements.

The purpose of the Venn diagram is to show the relationship between any two or more sets ....etc

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Fuzzy Logic and Fuzzy Sets 


History


Lotfi Zadeh, at the University of California at Berkeley, first presented fuzzy logic in the mid-1960's.

Zadeh developed fuzzy logic as a way of processing data. Instead of requiring a data element to be either a member or non-member of a set, he introduced the idea of partial set membership.

In 1974 Mamdani and Assilian used fuzzy logic to regulate a steam engine.

In 1985 researchers at Bell laboratories ....etc

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GRID SERVICE DISCOVERY WITH ROUGH SETS-- KNOWLEDGE AND DATA ENGINEERING

Abstract: A rough set is a formal approximation of a crisp set (conventional set) in terms of a pair of sets which give the lower and the upper approximation of the original set. The lower and upper approximation sets themselves are crisp sets in the standard version of rough set theory, but in other variations, the approximating sets may be fuzzy sets as well. The computational grid is rapidly evolving into a service-oriented computing infrastructure that facilitates r ....etc

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GRID SERVICE DISCOVERY WITH ROUGH SETS
Abstract: A rough set is a formal approximation of a crisp set (conventional set) in terms of a pair of sets which give the lower and the upper approximation of the original set. The lower and upper approximation sets themselves are crisp sets in the standard version of rough set theory, but in other variations, the approximating sets may be fuzzy sets as well. The computational grid is rapidly evolving into a service-oriented computing infrastructure that facilitates resource sharing and large-scale prob ....etc

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Using Texture and Shape Features to retrieve sets of similar Medical Images (Java)
IEEE Proceedings on Biomedical Engineering - Applications, Basis & Communications, Volume 15, No. 5,

Abstract:
In this paper, a novel scheme has been proposed for image retrieval task using the feature extracted directly from a compressed or uncompressed image. The texture information is first extracted by exploiting the multi-resolution nature of wavelet decomposition, which represent the horizontal, vertical and diagonal frequency distribution of ....etc

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sir,
i need the document for privacy preserving decision tree learning using unrealized datasets. ....etc

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Connected Dominating Sets (CDSs) can serve as virtual backbones for wireless networks. A smaller virtual backbone incurs less maintenance overhead. Unfortunately, computing a minimum size CDS is NP-hard, and thus most researchers in this area concentrate on how to construct smaller CDSs. However, people neglected other important metrics of network, such as diameter and average hop distances between two communication parties. In this paper, we investigate the problem of constructing quality CDS in terms of size, diameter, and Average Backbone Pa ....etc

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Abstract—Privacy preservation is important for machine learning and data mining, but measures designed to protect private information often result in a trade-off: reduced utility of the training samples. This paper introduces a privacy preserving approach that can be applied to decision tree learning, without concomitant loss of accuracy. It describes an approach to the preservation of the privacy of collected data samples in cases where information from the sample database has been partially lost. This approach converts the original sample d ....etc

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