data mining and machine learning lab document clustering via matrix representation xufei wang,...
Post on 15-Jan-2016
220 views
TRANSCRIPT
Data Mining and Machine Learning Lab
Document Clustering via Matrix Representation
Xufei Wang,
Jiliang Tang and Huan LiuArizona State University
News Article
• Lead Paragraph
• Explanations
• Additional Information
Research Paper
• Introduction• Related Work• Problem Statement• Solution• Experiment• Conclusion
Book
• Chapters
• References
• Appendix
Document Organization
• Not randomly organized
• Put relevant content together
• Logically independent segments
5
Matrix Space Model
• Represent a document as a matrix–Segment–Term
• Each segment is a vector of terms– Terms + frequency
6
Vector Space Model
• Oversimplify a document– Mixing topics– Word order is lost– Susceptible to noise
7
TdNddd wwwV ),,,( ,,2,1
An Example
• The IEEE International Conference on Data Mining series (ICDM) has established itself as the world's premier research conference in data mining. It provides an international forum for presentation of original research results, as well as exchange and dissemination of innovative, practical development experiences. The conference covers all aspects of data mining, including algorithms, software and systems, and applications.
• Vancouver is a coastal seaport city on the mainland of British Columbia, Canada. It is the hub of Greater Vancouver, which, with over 2.3 million residents, is the third most populous metropolitan area in the country, and the most populous in Western Canada.
Matrix Space Model
• Segment 1
• Segment 2
conference data mining research international
3 3 3 2 2
vancouver populous canada metropolitan columbia
2 2 2 1 1
Vector Space Model
conference data mining research international canada vancouver
3 3 3 2 2 2 2
Pros of a Matrix Representation
• Interpretation: – Segments vs. topics
• Finer granularity for data management– Segments vs. document
• Multiple or Single class labels– Flexibility
11
Verify the Effectiveness via Clustering
• Information Retrieval– Indexing based on segments
• Classification– New approaches based on Matrix inputs
• Clustering– New approaches based on Matrix inputs
12
A Graphical Interpretation
Step 1: Obtaining Segments
• Many approaches for segmentation– Terms– Sentences (Choi et al. 2000)– Paragraphs (Tagarelli et al. 2008)
• Determining the number of segments– Open research problem
14
Step 2: Latent Topic Extraction
• Non-negative Matrix Approximation (NMA)
• LMi represents the probability of a term belonging to a latent topic
• MiRT represents the probability of a segment belonging to a latent topic
15
n
iF
Tii
RRMMLL
RLMA
cii
r0
2
0:0:0:
2
21
1min
Step 3: Clustering
• Un-overlapping clustering
• Overlapping clustering
16
jiji
ii
k
dd
ccentroidd2)(min
ij
ijk
ccentroidd2)(min
Datasets
• 20newsgroup– 20 classes– 6,038 documents
• Reuters-21578– 26 clusters– 1,964 documents
• Classic– 3 clusters– 1,486 documents
17
Experimental Method
• Generate Datasets (by specifying k)
• Evaluate the accuracy
• Repeat
18
Number of Latent Topics
Number of Segments
Comparative Study
Conclusion
• Proposing a matrix representation for documents
• Significant improvements with MSM
• Information Retrieval, classification tasks
22
Questions
24
25