Knowledge Discovery from Data (TKDD)


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ACM Transactions on Knowledge Discovery from Data (TKDD) - Special Issue on ACM SIGKDD 2012, Volume 7 Issue 3, September 2013

Introduction to the Special Issue ACM SIGKDD 2012
Deepak Agarwal, Rich Caruana, Jian Pei, Ke Wang
Article No.: 9
DOI: 10.1145/2513092.2513093

Addressing Big Data Time Series: Mining Trillions of Time Series Subsequences Under Dynamic Time Warping
Thanawin Rakthanmanon, Bilson Campana, Abdullah Mueen, Gustavo Batista, Brandon Westover, Qiang Zhu, Jesin Zakaria, Eamonn Keogh
Article No.: 10
DOI: 10.1145/2500489

Most time series data mining algorithms use similarity search as a core subroutine, and thus the time taken for similarity search is the bottleneck for virtually all time series data mining algorithms, including classification, clustering, motif...

PathSelClus: Integrating Meta-Path Selection with User-Guided Object Clustering in Heterogeneous Information Networks
Yizhou Sun, Brandon Norick, Jiawei Han, Xifeng Yan, Philip S. Yu, Xiao Yu
Article No.: 11
DOI: 10.1145/2500492

Real-world, multiple-typed objects are often interconnected, forming heterogeneous information networks. A major challenge for link-based clustering in such networks is their potential to generate many different results, carrying rather diverse...

Active Sampling for Entity Matching with Guarantees
Kedar Bellare, Suresh Iyengar, Aditya Parameswaran, Vibhor Rastogi
Article No.: 12
DOI: 10.1145/2500490

In entity matching, a fundamental issue while training a classifier to label pairs of entities as either duplicates or nonduplicates is the one of selecting informative training examples. Although active learning presents an attractive solution to...

Batch Mode Active Sampling Based on Marginal Probability Distribution Matching
Rita Chattopadhyay, Zheng Wang, Wei Fan, Ian Davidson, Sethuraman Panchanathan, Jieping Ye
Article No.: 13
DOI: 10.1145/2513092.2513094

Active Learning is a machine learning and data mining technique that selects the most informative samples for labeling and uses them as training data; it is especially useful when there are large amount of unlabeled data and labeling them is...

Instance Annotation for Multi-Instance Multi-Label Learning
Forrest Briggs, Xiaoli Z. Fern, Raviv Raich, Qi Lou
Article No.: 14
DOI: 10.1145/2500491

Multi-instance multi-label learning (MIML) is a framework for supervised classification where the objects to be classified are bags of instances associated with multiple labels. For example, an image can be represented as a bag of segments and...

Parallel Field Ranking
Ming Ji, Binbin Lin, Xiaofei He, Deng Cai, Jiawei Han
Article No.: 15
DOI: 10.1145/2513092.2513096

Recently, ranking data with respect to the intrinsic geometric structure (manifold ranking) has received considerable attentions, with encouraging performance in many applications in pattern recognition, information retrieval and recommendation...