Titlebook: Web Technologies Research and Development - APWeb 2005; 7th Asia-Pacific Web Yanchun Zhang,Katsumi Tanaka,Minglu Li Conference proceedings

亚当心理阴影

A Unified Probabilistic Framework for Clustering Correlated Heterogeneous Web Objectsnto account the relationship between data objects, but they either integrate content and link features into a unified feature space or apply a hard clustering algorithm, making it difficult to fully utilize the correlated information over the heterogeneous Web objects. In this paper, we propose a no

Horizon

CLINCH: Clustering Incomplete High-Dimensional Data for Data Mining Applicationning application, clustering incomplete high-dimensional data has becoming more and more useful. Motivated by these limits, we develop a novel algorithm ., which could produce fine clusters on incomplete high-dimensional data space. To handle missing attributes, CLINCH employs a prediction method th

洁净

CLINCH: Clustering Incomplete High-Dimensional Data for Data Mining Applicationning application, clustering incomplete high-dimensional data has becoming more and more useful. Motivated by these limits, we develop a novel algorithm ., which could produce fine clusters on incomplete high-dimensional data space. To handle missing attributes, CLINCH employs a prediction method th

刺耳

MENT

Topic Discovery from Document Using Ant-Based Clustering Combinationdocument is represented as a vector of features in a vector space model. Then a hypergraph model is used to combine the clusterings produced by three kinds of ant-based algorithms with different moving speed. Finally, the topic of each cluster is extracted by re-computing the term weights. Test resu

AWE

镇压

询问

Limited

A Similarity Reinforcement Algorithm for Heterogeneous Web Pagesel or Latent Semantic Index only used single relationship to measure the similarity of data objects. In this paper, we first use an Intra- and Inter- Type Relationship Matrix (IITRM) to represent a set of heterogeneous data objects and their inter-relationships. Then, we propose a novel similarity-c

elastic