Data Mining - Concepts and Techniques.

저작근 근전도에 관한 정상교합자와 ii급 부정교합자의 비교 연구

Though research on the Semantic Web has progressed at a steady pace, its promise has yet to be realized. One major difficulty is that, by its very nature, the Semantic Web is a large, uncensored system to which anyone may contribute. This raises the question of how much credence to give each source. We cannot expect each user to know the trustworthiness of each source, nor would we want to assign top-down or global credibility values due to the subjective nature of trust. We tackle this problem by employing a web of trust, in which each user maintains trusts in a small number of other users. We then compose these trusts into trust values for all other users. The result of our computation is not an agglomerate "trustworthiness" of each user. Instead, each user receives a personalized set of trusts, which may vary widely from person to person. We define properties for combination functions which merge such trusts, and define a class of functions for which merging may be done locally while maintaining these properties. We give examples of specific functions and apply them to data from Epinions and our BibServ bibliography server. Experiments confirm that the methods are robust to noise, and do not put unreasonable expectations on users. We hope that these methods will help move the Semantic Web closer to fulfilling its promise.

Trust management for the Semantic Web

Feature selection in mixed data

Spatiotemporal patterns of remotely sensed PM2.5 concentration in China from 1999 to 2011

An efficient instance selection algorithm for k nearest neighbor regression

http://ccc.inaoep.mx/~ariel/2013/Determining%20the%20number%20of%20clusters%20using%20information%20entropy%20for%20mixed%20data.pdf

Determining the number of clusters using information entropy for mixed data

A community detection algorithm based on graph compression for large-scale social networks

A weighting k-modes algorithm for subspace clustering of categorical data

Clustering ensemble selection for categorical data based on internal validity indices

Xingwang Zhao

Papers

An efficient instance selection algorithm for k nearest neighbor regression

Determining the number of clusters using information entropy for mixed data

A community detection algorithm based on graph compression for large-scale social networks

A weighting k-modes algorithm for subspace clustering of categorical data

Clustering ensemble selection for categorical data based on internal validity indices