Knowledge Discovery from Data (TKDD)


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ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 10 Issue 3, February 2016

Mining User Development Signals for Online Community Churner Detection
Matthew Rowe
Article No.: 21
DOI: 10.1145/2798730

Churners are users who stop using a given service after previously signing up. In the domain of telecommunications and video games, churners represent a loss of revenue as a user leaving indicates that they will no longer pay for the service. In...

Put Three and Three Together: Triangle-Driven Community Detection
Arnau Prat-Pérez, David Dominguez-Sal, Josep-M. Brunat, Josep-Lluis Larriba-Pey
Article No.: 22
DOI: 10.1145/2775108

Community detection has arisen as one of the most relevant topics in the field of graph data mining due to its applications in many fields such as biology, social networks, or network traffic analysis. Although the existing metrics used to...

Multimodal Data Mining in a Multimedia Database Based on Structured Max Margin Learning
Zhen Guo, Zhongfei (Mark) Zhang, Eric P. Xing, Christos Faloutsos
Article No.: 23
DOI: 10.1145/2742549

Mining knowledge from a multimedia database has received increasing attentions recently since huge repositories are made available by the development of the Internet. In this article, we exploit the relations among different modalities in a...

Do Anesthesiologists Know What They Are Doing? Mining a Surgical Time-Series Database to Correlate Expert Assessment with Outcomes
Risa B. Myers, John C. Frenzel MD, Joseph R. Ruiz Md, Christopher M. Jermaine
Article No.: 24
DOI: 10.1145/2822897

Anesthesiologists are taught to carefully manage patient vital signs during surgery. Unfortunately, there is little empirical evidence that vital sign management, as currently practiced, is correlated with patient outcomes. We seek to validate or...

Collective Graph Identification
Galileo Mark Namata, Ben London, Lise Getoor
Article No.: 25
DOI: 10.1145/2818378

Data describing networks—such as communication networks, transaction networks, disease transmission networks, collaboration networks, etc.—are becoming increasingly available. While observational data can be useful, it often only hints...

Mining Influencers Using Information Flows in Social Streams
Karthik Subbian, Charu Aggarwal, Jaideep Srivastava
Article No.: 26
DOI: 10.1145/2815625

The problem of discovering information flow trends in social networks has become increasingly relevant due to the increasing amount of content in online social networks, and its relevance as a tool for research into the content trends analysis in...

Toward Generalizing the Unification with Statistical Outliers: The Gradient Outlier Factor Measure
Fabrizio Angiulli, Fabio Fassetti
Article No.: 27
DOI: 10.1145/2829956

In this work, we introduce a novel definition of outlier, namely the Gradient Outlier Factor (or GOF), with the aim to provide a definition that unifies with the statistical one on some standard distributions but has a different behavior in the...

DeltaCon: Principled Massive-Graph Similarity Function with Attribution
Danai Koutra, Neil Shah, Joshua T. Vogelstein, Brian Gallagher, Christos Faloutsos
Article No.: 28
DOI: 10.1145/2824443

How much has a network changed since yesterday? How different is the wiring of Bob’s brain (a left-handed male) and Alice’s brain (a right-handed female), and how is it different? Graph similarity with given node correspondence, i.e.,...

Mining Product Adopter Information from Online Reviews for Improving Product Recommendation
Wayne Xin Zhao, Jinpeng Wang, Yulan He, Ji-Rong Wen, Edward Y. Chang, Xiaoming Li
Article No.: 29
DOI: 10.1145/2842629

We present in this article an automated framework that extracts product adopter information from online reviews and incorporates the extracted information into feature-based matrix factorization for more effective product recommendation. In...

Adaptive Model Rules From High-Speed Data Streams
João Duarte, João Gama, Albert Bifet
Article No.: 30
DOI: 10.1145/2829955

Decision rules are one of the most expressive and interpretable models for machine learning. In this article, we present Adaptive Model Rules (AMRules), the first stream rule learning algorithm for regression problems. In AMRules, the antecedent...

Synchronization-Core-Based Discovery of Processes with Decomposable Cyclic Dependencies
Faming Lu, Qingtian Zeng, Hua Duan
Article No.: 31
DOI: 10.1145/2845086

Traditional process discovery techniques mine process models based upon event traces giving little consideration to workflow relevant data recorded in event logs. The neglect of such information usually leads to incorrect discovered models,...

An Efficient Algorithm For Weak Hierarchical Lasso
Yashu Liu, Jie Wang, Jieping Ye
Article No.: 32
DOI: 10.1145/2791295

Linear regression is a widely used tool in data mining and machine learning. In many applications, fitting a regression model with only linear effects may not be sufficient for predictive or explanatory purposes. One strategy that has recently...