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Knowledge Discovery from Data (TKDD)

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ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 9 Issue 1, October 2014

An Optimization Framework for Combining Ensembles of Classifiers and Clusterers with Applications to Nontransductive Semisupervised Learning and Transfer Learning
Ayan Acharya, Eduardo R. Hruschka, Joydeep Ghosh, Sreangsu Acharyya
Article No.: 1
DOI: 10.1145/2601435

Unsupervised models can provide supplementary soft constraints to help classify new “target” data because similar instances in the target set are more likely to share the same class label. Such models can also help detect possible...

A Framework for Exploiting Local Information to Enhance Density Estimation of Data Streams
Arnold P. Boedihardjo, Chang-Tien Lu, Bingsheng Wang
Article No.: 2
DOI: 10.1145/2629618

The Probability Density Function (PDF) is the fundamental data model for a variety of stream mining algorithms. Existing works apply the standard nonparametric Kernel Density Estimator (KDE) to approximate the PDF of data streams. As a result, the...

Bayesian Variable Selection in Linear Regression in One Pass for Large Datasets
Carlos Ordonez, Carlos Garcia-Alvarado, Veerabhadaran Baladandayuthapani
Article No.: 3
DOI: 10.1145/2629617

Bayesian models are generally computed with Markov Chain Monte Carlo (MCMC) methods. The main disadvantage of MCMC methods is the large number of iterations they need to sample the posterior distributions of model parameters, especially for large...

Structured Sparse Boosting for Graph Classification
Hongliang Fei, Jun Huan
Article No.: 4
DOI: 10.1145/2629328

Boosting is a highly effective algorithm that produces a linear combination of weak classifiers (a.k.a. base learners) to obtain high-quality classification models. In this article, we propose a generalized logit boost algorithm in which base...

GBAGC: A General Bayesian Framework for Attributed Graph Clustering
Zhiqiang Xu, Yiping Ke, Yi Wang, Hong Cheng, James Cheng
Article No.: 5
DOI: 10.1145/2629616

Graph clustering, also known as community detection, is a long-standing problem in data mining. In recent years, with the proliferation of rich attribute information available for objects in real-world graphs, how to leverage not only structural...

Uncovering Hierarchical and Overlapping Communities with a Local-First Approach
Michele Coscia, Giulio Rossetti, Fosca Giannotti, Dino Pedreschi
Article No.: 6
DOI: 10.1145/2629511

Community discovery in complex networks is the task of organizing a network’s structure by grouping together nodes related to each other. Traditional approaches are based on the assumption that there is a global-level organization in the...

A Generic Multilabel Learning-Based Classification Algorithm Recommendation Method
Guangtao Wang, Qinbao Song, Xueying Zhang, Kaiyuan Zhang
Article No.: 7
DOI: 10.1145/2629474

As more and more classification algorithms continue to be developed, recommending appropriate algorithms to a given classification problem is increasingly important. This article first distinguishes the algorithm recommendation methods by two...