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KDD
2006
ACM
145views Data Mining» more  KDD 2006»
15 years 11 months ago
Deriving quantitative models for correlation clusters
Correlation clustering aims at grouping the data set into correlation clusters such that the objects in the same cluster exhibit a certain density and are all associated to a comm...
Arthur Zimek, Christian Böhm, Elke Achtert, H...
92
Voted
KDD
2009
ACM
180views Data Mining» more  KDD 2009»
15 years 3 months ago
Parallel community detection on large networks with propinquity dynamics
Graphs or networks can be used to model complex systems. Detecting community structures from large network data is a classic and challenging task. In this paper, we propose a nove...
Yuzhou Zhang, Jianyong Wang, Yi Wang, Lizhu Zhou
WWW
2007
ACM
15 years 12 months ago
Why we search: visualizing and predicting user behavior
The aggregation and comparison of behavioral patterns on the WWW represent a tremendous opportunity for understanding past behaviors and predicting future behaviors. In this paper...
Eytan Adar, Daniel S. Weld, Brian N. Bershad, Stev...
KDD
2006
ACM
198views Data Mining» more  KDD 2006»
15 years 11 months ago
Estimating the global pagerank of web communities
Localized search engines are small-scale systems that index a particular community on the web. They offer several benefits over their large-scale counterparts in that they are rel...
Jason V. Davis, Inderjit S. Dhillon
KDD
2004
ACM
150views Data Mining» more  KDD 2004»
15 years 11 months ago
Complete This Puzzle: A Connectionist Approach to Accurate Web Recommendations Based on a Committee of Predictors
Abstract. We present a Context Ultra-Sensitive Approach based on two-step Recommender systems (CUSA-2step-Rec). Our approach relies on a committee of profile-specific neural networ...
Olfa Nasraoui, Mrudula Pavuluri