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IEEE Transactions on Knowledge and Data Engineering

Issue 2 • Date Feb. 2012

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  • [Front cover]

    Publication Year: 2012, Page(s): c1
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  • [Cover 2]

    Publication Year: 2012, Page(s): c2
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  • State of the Journal

    Publication Year: 2012, Page(s):193 - 196
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  • A Multidimensional Sequence Approach to Measuring Tree Similarity

    Publication Year: 2012, Page(s):197 - 208
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1518 KB) | HTML iconHTML

    Tree is one of the most common and well-studied data structures in computer science. Measuring the similarity of such structures is key to analyzing this type of data. However, measuring tree similarity is not trivial due to the inherent complexity of trees and the ensuing large search space. Tree kernel, a state of the art similarity measurement of trees, represents trees as vectors in a feature ... View full abstract»

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  • Agglomerative Mean-Shift Clustering

    Publication Year: 2012, Page(s):209 - 219
    Cited by:  Papers (7)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2542 KB) | HTML iconHTML

    Mean-Shift (MS) is a powerful nonparametric clustering method. Although good accuracy can be achieved, its computational cost is particularly expensive even on moderate data sets. In this paper, for the purpose of algorithmic speedup, we develop an agglomerative MS clustering method along with its performance analysis. Our method, namely Agglo-MS, is built upon an iterative query set compression m... View full abstract»

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  • Answering General Time-Sensitive Queries

    Publication Year: 2012, Page(s):220 - 235
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1411 KB)

    Time is an important dimension of relevance for a large number of searches, such as over blogs and news archives. So far, research on searching over such collections has largely focused on locating topically similar documents for a query. Unfortunately, topic similarity alone is not always sufficient for document ranking. In this paper, we observe that, for an important class of queries that we ca... View full abstract»

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  • BibPro: A Citation Parser Based on Sequence Alignment

    Publication Year: 2012, Page(s):236 - 250
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2418 KB) | HTML iconHTML Multimedia Media

    Dramatic increase in the number of academic publications has led to growing demand for efficient organization of the resources to meet researchers' needs. As a result, a number of network services have compiled databases from the public resources scattered over the Internet. However, publications by different conferences and journals adopt different citation styles. It is an interesting problem to... View full abstract»

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  • Discover Dependencies from Data—A Review

    Publication Year: 2012, Page(s):251 - 264
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1268 KB) | HTML iconHTML

    Functional and inclusion dependency discovery is important to knowledge discovery, database semantics analysis, database design, and data quality assessment. Motivated by the importance of dependency discovery, this paper reviews the methods for functional dependency, conditional functional dependency, approximate functional dependency, and inclusion dependency discovery in relational databases an... View full abstract»

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  • Effective and Efficient Shape-Based Pattern Detection over Streaming Time Series

    Publication Year: 2012, Page(s):265 - 278
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1376 KB) | HTML iconHTML

    Existing distance measures of time series such as the euclidean distance, DTW, and EDR are inadequate in handling certain degrees of amplitude shifting and scaling variances of data items. We propose a novel distance measure of time series, Spatial Assembling Distance (SpADe), that is able to handle noisy, shifting, and scaling in both temporal and amplitude dimensions. We further apply the SpADe ... View full abstract»

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  • Mining Low-Support Discriminative Patterns from Dense and High-Dimensional Data

    Publication Year: 2012, Page(s):279 - 294
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1107 KB) | HTML iconHTML

    Discriminative patterns can provide valuable insights into data sets with class labels, that may not be available from the individual features or the predictive models built using them. Most existing approaches work efficiently for sparse or low-dimensional data sets. However, for dense and high-dimensional data sets, they have to use high thresholds to produce the complete results within limited ... View full abstract»

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  • On Group Nearest Group Query Processing

    Publication Year: 2012, Page(s):295 - 308
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1426 KB) | HTML iconHTML

    Given a data point set D, a query point set Q, and an integer k, the Group Nearest Group (GNG) query finds a subset ω (|ω| ≤ k)of points from Dsuch that the total distance from all points in Q to the nearest point in ω is not greater than any other subset ω' (|ω'| ≤ k) of points in D. GNG query is a partition-based clustering problem which can be fo... View full abstract»

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  • On the Deep Order-Preserving Submatrix Problem: A Best Effort Approach

    Publication Year: 2012, Page(s):309 - 325
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1769 KB) | HTML iconHTML

    Order-preserving submatrix (OPSM) has been widely accepted as a biologically meaningful cluster model, capturing the general tendency of gene expression across a subset of experiments. In an OPSM, the expression levels of all genes induce the same linear ordering of the experiments. The OPSM problem is to discover those statistically significant OPSMs from a given data matrix. The problem is reduc... View full abstract»

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  • On the Spectral Characterization and Scalable Mining of Network Communities

    Publication Year: 2012, Page(s):326 - 337
    Cited by:  Papers (4)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1565 KB) | HTML iconHTML

    Network communities refer to groups of vertices within which their connecting links are dense but between which they are sparse. A network community mining problem (or NCMP for short) is concerned with the problem of finding all such communities from a given network. A wide variety of applications can be formulated as NCMPs, ranging from social and/or biological network analysis to web mining and ... View full abstract»

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  • Outsourced Similarity Search on Metric Data Assets

    Publication Year: 2012, Page(s):338 - 352
    Cited by:  Papers (7)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1211 KB) | HTML iconHTML

    This paper considers a cloud computing setting in which similarity querying of metric data is outsourced to a service provider. The data is to be revealed only to trusted users, not to the service provider or anyone else. Users query the server for the most similar data objects to a query example. Outsourcing offers the data owner scalability and a low-initial investment. The need for privacy may ... View full abstract»

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  • Privacy Preserving Decision Tree Learning Using Unrealized Data Sets

    Publication Year: 2012, Page(s):353 - 364
    Cited by:  Papers (8)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (11062 KB)

    Privacy preservation is important for machine learning and data mining, but measures designed to protect private information often result in a trade-off: reduced utility of the training samples. This paper introduces a privacy preserving approach that can be applied to decision tree learning, without concomitant loss of accuracy. It describes an approach to the preservation of the privacy of colle... View full abstract»

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  • Subspace Similarity Search under {rm L}_p-Norm

    Publication Year: 2012, Page(s):365 - 382
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1304 KB) | HTML iconHTML Multimedia Media

    Similarity search has been widely used in many applications such as information retrieval, image data analysis, and time-series matching. Previous work on similarity search usually consider the search problem in the full space. In this paper, however, we tackle a problem, subspace similarity search, which finds all data objects that match with a query object in the subspace instead of the original... View full abstract»

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  • IEEE Computer Society OnlinePlus Coming Soon to TKDE

    Publication Year: 2012, Page(s): 383
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  • What's new in Transactions [advertisement]

    Publication Year: 2012, Page(s): 384
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  • [Cover3]

    Publication Year: 2012, Page(s): c3
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  • [Cover 4]

    Publication Year: 2012, Page(s): c4
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Aims & Scope

IEEE Transactions on Knowledge and Data Engineering (TKDE) informs researchers, developers, managers, strategic planners, users, and others interested in state-of-the-art and state-of-the-practice activities in the knowledge and data engineering area.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Jian Pei
Simon Fraser University

Associate Editor-in-Chief
Xuemin Lin
University of New South Wales

Associate Editor-in-Chief
Lei Chen
Hong Kong University of Science and Technology