<--- Back to Details
First PageDocument Content
Information science / Data mining / Information / Natural language processing / Statistical natural language processing / Document clustering / Cluster analysis / Topic model / Information retrieval / UPGMA
Date: 2016-06-28 04:30:57
Information science
Data mining
Information
Natural language processing
Statistical natural language processing
Document clustering
Cluster analysis
Topic model
Information retrieval
UPGMA

Microsoft Word - wch.V1.5.print.doc

Add to Reading List

Source URL: www.sogou.com

Download Document from Source Website

File Size: 396,36 KB

Share Document on Facebook

Similar Documents

Recent Developments in Document Clustering Nicholas O. Andrews and Edward A. Fox Department of Computer Science, Virginia Tech, Blacksburg, VA 24060 {nandrews, fox}@vt.edu October 16, 2007

DocID: 1thA2 - View Document

Combining a Double Clustering Approach with Sentence Simplification to Produce Highly Informative Multi-document Summaries Sara Botelho Silveira and Ant´onio Branco University of Lisbon Edif´ıcio C6, Departamento de

DocID: 1stOV - View Document

Using a Wikipedia-based Semantic Relatedness Measure for Document Clustering Majid Yazdani Idiap Research Institute and EPFL Centre du Parc, Rue MarconiMartigny, Switzerland

DocID: 1sjIn - View Document

Software / Computing / Portable media players / Music information retrieval / Music software / IPod software / Last.fm / Playlist / Non-negative matrix factorization / Document clustering / IPod / Recommender system

One-Touch Access to Music on Mobile Devices Dominik Schnitzer1,2 ,Tim Pohle1 , Peter Knees1 , and Gerhard Widmer1,2 1 Department of Computational Perception, Johannes Kepler University Linz, Austria 2

DocID: 1rtSc - View Document

Statistics / Cluster analysis / Information science / Data analysis / Data mining / Geostatistics / Hierarchical clustering / K-means clustering / Document clustering / Biclustering / Consensus clustering / Determining the number of clusters in a data set

V-Measure: A conditional entropy-based external cluster evaluation measure Andrew Rosenberg and Julia Hirschberg Department of Computer Science Columbia University New York, NY 10027

DocID: 1rtvl - View Document