9 results match your criteria ACM Transactions on Knowledge Discovery from Data[Journal]

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Cross-Dependency Inference in Multi-Layered Networks: A Collaborative Filtering Perspective.

ACM Trans Knowl Discov Data 2017 Aug;11(4)

University at Buffalo.

The increasingly connected world has catalyzed the fusion of networks from different domains, which facilitates the emergence of a new network model-multi-layered networks. Examples of such kind of network systems include critical infrastructure networks, biological systems, organization-level collaborations, cross-platform e-commerce, and so forth. One crucial structure that distances multi-layered network from other network models is its cross-layer dependency, which describes the associations between the nodes from different layers. Read More

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CGC: A Flexible and Robust Approach to Integrating Co-Regularized Multi-Domain Graph for Clustering.

ACM Trans Knowl Discov Data 2016 Jul;10(4)

University of California, Los Angeles.

Multi-view graph clustering aims to enhance clustering performance by integrating heterogeneous information collected in different domains. Each domain provides a different view of the data instances. Leveraging cross-domain information has been demonstrated an effective way to achieve better clustering results. Read More

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Scalable and Axiomatic Ranking of Network Role Similarity.

ACM Trans Knowl Discov Data 2014 Feb;8(1)

Lanzhou University.

A key task in analyzing social networks and other complex networks is role analysis: describing and categorizing nodes according to how they interact with other nodes. Two nodes have the same role if they interact with sets of neighbors. The most fundamental role equivalence is automorphic equivalence. Read More

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February 2014

Social Trust Prediction Using Heterogeneous Networks.

ACM Trans Knowl Discov Data 2013 Nov;7(4):17

University of Texas at Arlington.

Along with increasing popularity of social websites, online users rely more on the trustworthiness information to make decisions, extract and filter information, and tag and build connections with other users. However, such social network data often suffer from severe data sparsity and are not able to provide users with enough information. Therefore, trust prediction has emerged as an important topic in social network research. Read More

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November 2013

Addressing Big Data Time Series: Mining Trillions of Time Series Subsequences Under Dynamic Time Warping.

ACM Trans Knowl Discov Data 2013 Sep;7(3)

University of California Riverside.

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 discovery, anomaly detection, and so on. The difficulty of scaling a search to large datasets explains to a great extent why most academic work on time series data mining has plateaued at considering a few millions of time series objects, while much of industry and science sits on billions of time series objects waiting to be explored. In this work we show that by using a combination of four novel ideas we can search and mine massive time series for the first time. Read More

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September 2013

Learning Incoherent Sparse and Low-Rank Patterns from Multiple Tasks.

ACM Trans Knowl Discov Data 2012 Feb;5(4):22

Arizona State University.

We consider the problem of learning incoherent sparse and low-rank patterns from multiple tasks. Our approach is based on a linear multi-task learning formulation, in which the sparse and low-rank patterns are induced by a cardinality regularization term and a low-rank constraint, respectively. This formulation is non-convex; we convert it into its convex surrogate, which can be routinely solved via semidefinite programming for small-size problems. Read More

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February 2012

Motif Discovery in Physiological Datasets: A Methodology for Inferring Predictive Elements.

ACM Trans Knowl Discov Data 2010 Jan;4(1)

University of Michigan.

In this article, we propose a methodology for identifying predictive physiological patterns in the absence of prior knowledge. We use the principle of conservation to identify activity that consistently precedes an outcome in patients, and describe a two-stage process that allows us to efficiently search for such patterns in large datasets. This involves first transforming continuous physiological signals from patients into symbolic sequences, and then searching for patterns in these reduced representations that are strongly associated with an outcome. Read More

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January 2010

Author Name Disambiguation in MEDLINE.

ACM Trans Knowl Discov Data 2009 Jul;3(3)

University of Illinois at Chicago.

BACKGROUND: We recently described "Author-ity," a model for estimating the probability that two articles in MEDLINE, sharing the same author name, were written by the same individual. Features include shared title words, journal name, coauthors, medical subject headings, language, affiliations, and author name features (middle initial, suffix, and prevalence in MEDLINE). Here we test the hypothesis that the Author-ity model will suffice to disambiguate author names for the vast majority of articles in MEDLINE. Read More

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Developmental Stage Annotation of Drosophila Gene Expression Pattern Images via an Entire Solution Path for LDA.

ACM Trans Knowl Discov Data 2008 Mar;2(1)

Center for Evolutionary Functional Genomics and Department of Computer Science and Engineering, Arizona State University, Tempe, AZ 85287, Email:

Gene expression in a developing embryo occurs in particular cells (spatial patterns) in a time-specific manner (temporal patterns), which leads to the differentiation of cell fates. Images of a Drosophila melanogaster embryo at a given developmental stage, showing a particular gene expression pattern revealed by a gene-specific probe, can be compared for spatial overlaps. The comparison is fundamentally important to formulating and testing gene interaction hypotheses. Read More

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