a generalized cluster centroid based classiﬁer for text categorization

Intelligent Database Systems Lab

Presenter : BEI-YI JIANG

Authors : GUANSONG PANG, SHENGYI JIANG

2013. INFORMATION PROCESSING AND MANAGEMENT

A generalized cluster centroid based classifier for text categorization


OutlinesMotivationObjectivesMethodologyExperimentsConclusionsComments


Motivation

• KNN− With the exponential growth of online textual

information, how to organize text data effectively and efficiently has become an important and demanding issue.

• Rocchio− Fails to obtain an expressive categorization

model due to its inherent linear separability assumption.


Objectives

• To strengthen the expressiveness of the Rocchio model.

• Employ the improved Rocchio model to speed up the categorization process of KNN.


Methodology

• KNN

• Rocchio


Methodology


Methodology

• GCC

• Determine the threshold


Experiments


Conclusions

• strengthen the expressiveness of the Rocchio model• GCCC and its variants achieve impressive

performance• obtain near linear time complexity in modeling• GCCC’s modeling stage is more time-consuming


Comments• Advantages

-relatively stable-favorable performance

• Applications-online categorization

a generalized cluster centroid based classiﬁer for text categorization

Documents

improved rocchio model

rocchio modelgccc

linear time complexity

information processing

text data

categorization process

generalized cluster

beiyi jiangauthors