IEEE Transactions on Knowledge and Data Engineering
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Year: 2008 | Volume: 20 | Issue: 6
C-TREND: Temporal Cluster Graphs for Identifying and Visualizing Trends in Multiattribute Transactional Data
Publication Year: 2008,Page(s):721 - 735
Organizations and firms are capturing increasingly more data about their customers, suppliers, competitors, and business environment. Most of this data is multi-attribute (multi-dimensional) and temporal in nature. Data mining and business intelligence techniques are often used to discover patterns in such data; however, mining temporal relationships typically is a complex task. We propose a new d...Show More
Algorithms for Storytelling
Deept Kumar;Naren Ramakrishnan;Richard F. Helm;Malcolm Potts
Publication Year: 2008,Page(s):736 - 751
Cited by: Papers (25)
We formulate a new data mining problem called storytelling as a generalization of redescription mining. In traditional redescription mining, we are given a set of objects and a collection of subsets defined over these objects. The goal is to view the set system as a vocabulary and identify two expressions in this vocabulary that induce the same set of objects. Storytelling, on the other hand, aims...Show More
Algorithms for Storytelling
Deept Kumar;Naren Ramakrishnan;Richard F. Helm;Malcolm Potts
Year: 2008 | Volume: 20 | Issue: 6
An Efficient Clustering Scheme to Exploit Hierarchical Data in Network Traffic Analysis
Publication Year: 2008,Page(s):752 - 767
Cited by: Papers (32)
There is significant interest in the data mining and network management communities about the need to improve existing techniques for clustering multivariate network traffic flow records so that we can quickly infer underlying traffic patterns. In this paper, we investigate the use of clustering techniques to identify interesting traffic patterns from network traffic data in an efficient manner. W...Show More
Bounded Approximation: A New Criterion for Dimensionality Reduction Approximation in Similarity Search
Publication Year: 2008,Page(s):768 - 783
Cited by: Papers (4)
We examine the problem of efficient distance-based similarity search over high-dimensional data. We show that a promising approach to this problem is to reduce dimensions and allow fast approximation. Conventional reduction approaches, however, entail a significant shortcoming: The approximation volume extends across the dataspace, which causes overestimation of retrieval sets and impairs performa...Show More
Hardware-Enhanced Association Rule Mining with Hashing and Pipelining
Publication Year: 2008,Page(s):784 - 795
Cited by: Papers (26)
Generally speaking, to implement Apriori-based association rule mining in hardware, one has to load candidate itemsets and a database into the hardware. Since the capacity of the hardware architecture is fixed, if the number of candidate itemsets or the number of items in the database is larger than the hardware capacity, the items are loaded into the hardware separately. The time complexity of th...Show More
Truth Discovery with Multiple Conflicting Information Providers on the Web
Publication Year: 2008,Page(s):796 - 808
The World Wide Web has become the most important information source for most of us. Unfortunately, there is no guarantee for the correctness of information on the Web. Moreover, different websites often provide conflicting information on a subject, such as different specifications for the same product. In this paper, we propose a new problem, called Veracity, i.e., conformity to truth, which studi...Show More
Probabilistic Group Nearest Neighbor Queries in Uncertain Databases
Publication Year: 2008,Page(s):809 - 824
The importance of query processing over uncertain data has recently arisen due to its wide usage in many real-world applications. In the context of uncertain databases, previous works have studied many query types such as nearest neighbor query, range query, top-k query, skyline query, and similarity join. In this paper, we focus on another important query, namely, probabilistic group nearest neig...Show More
Probabilistic Group Nearest Neighbor Queries in Uncertain Databases
Year: 2008 | Volume: 20 | Issue: 6
A Signature-Based Indexing Method for Efficient Content-Based Retrieval of Relative Temporal Patterns
Publication Year: 2008,Page(s):825 - 835
Cited by: Papers (2)
A number of algorithms have been proposed for the discovery of temporal patterns. However, since the number of generated patterns can be large, selecting which patterns to analyze can be nontrivial. There is thus a need for algorithms and tools that can assist in the selection of discovered patterns so that subsequent analysis can be performed in an efficient and, ideally, interactive manner. In t...Show More
Evolutionary Optimization of File Assignment for a Large-Scale Video-on-Demand System
Jun Guo;Yi Wang;Kit-Sang Tang;Sammy Chan;Eric W.M. Wong;Peter Taylor;Moshe Zukerman
Publication Year: 2008,Page(s):836 - 850
Cited by: Papers (10)
We present a genetic algorithm for tackling a file assignment problem for a large-scale video-on-demand system. The file assignment problem is to find the optimal replication and allocation of movie files to disks so that the request blocking probability is minimized subject to capacity constraints. We adopt a divide-and-conquer strategy, where the entire solution space of file assignments is divi...Show More
Grid Service Discovery with Rough Sets
Publication Year: 2008,Page(s):851 - 862
Cited by: Papers (33)
The computational grid is rapidly evolving into a service-oriented computing infrastructure that facilitates resource sharing and large-scale problem solving over the Internet. Service discovery becomes an issue of vital importance in utilizing grid facilities. This paper presents ROSSE, a Rough sets-based search engine for grid service discovery. Building on the Rough sets theory, ROSSE is novel ...Show More
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Year: 2008 | Volume: 20 | Issue: 6
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Year: 2008 | Volume: 20 | Issue: 6
TKDE Information for authors
Year: 2008 | Volume: 20 | Issue: 6
Contact Information
Editor-in-Chief
Lei Chen
Hong Kong University of Science and Technology, Department of Computer Science and Engineering
Hong Kong
China
leichen@cse.ust.hkContact Information
Editor-in-Chief
Lei Chen
Hong Kong University of Science and Technology, Department of Computer Science and Engineering
Hong Kong
China
leichen@cse.ust.hkIEEE Account
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