]> BibSonomy publications for /author/Han http://www.bibsonomy.org/burst/author/Han BibSonomy BuRST Feed for /author/Han 2008-05-18T10:26:36+02:00 On Maximizing the Throughput of Convergecast in Wireless Sensor Networks. dblphttp://www.bibsonomy.org/bibtex/2e83aeb6010eb48913082b8dd2b133e0a/dblp dblp 2008-05-16T00:00:00+02:00 dblp
On Maximizing the Throughput of Convergecast in Wireless Sensor Networks.
Nai-Luen Lai and Chung-Ta King and Chun-Han Lin GPC 396-408 (2008)
to dblp by dblp on 2008-05-16 00:00:00 ]]> GPCconf/gpc/2008396-408Lecture Notes in Computer ScienceOn Maximizing the Throughput of Convergecast in Wireless Sensor Networks.50362008dblp 2008-05-16 00:00:00.0 A Construction of Peer-to-Peer Streaming System Based on Flexible Locality-Aware Overlay Networks. dblphttp://www.bibsonomy.org/bibtex/221732d1d808018704910e058c75b9b3a/dblp dblp 2008-05-16T00:00:00+02:00 dblp
A Construction of Peer-to-Peer Streaming System Based on Flexible Locality-Aware Overlay Networks.
Chih-Han Lai and Yu-Wei Chan and Yeh-Ching Chung GPC 296-307 (2008)
to dblp by dblp on 2008-05-16 00:00:00 ]]> GPCconf/gpc/2008296-307Lecture Notes in Computer ScienceA Construction of Peer-to-Peer Streaming System Based on Flexible Locality-Aware Overlay Networks.50362008dblp 2008-05-16 00:00:00.0 A Birthday Paradox for Markov Chains, with an Optimal Bound for Collision in the Pollard Rho Algorithm for Discrete Logarithm. dblphttp://www.bibsonomy.org/bibtex/26603814e8c43dae395abfa7f4a9f0c2b/dblp dblp 2008-05-16T00:00:00+02:00 dblp
A Birthday Paradox for Markov Chains, with an Optimal Bound for Collision in the Pollard Rho Algorithm for Discrete Logarithm.
Jeong Han Kim and Ravi Montenegro and Yuval Peres and Prasad Tetali ANTS 402-415 (2008)
to dblp by dblp on 2008-05-16 00:00:00 ]]> ANTSconf/ants/2008402-415Lecture Notes in Computer ScienceA Birthday Paradox for Markov Chains, with an Optimal Bound for Collision in the Pollard Rho Algorithm for Discrete Logarithm.50112008dblp 2008-05-16 00:00:00.0 SEM: Mining Spatial Events from the Web. dblphttp://www.bibsonomy.org/bibtex/20195b14ade3c8c1f6e31c480db97dc85/dblp dblp 2008-05-15T00:00:00+02:00 dblp
SEM: Mining Spatial Events from the Web.
Kaifeng Xu and Rui Li and Shenghua Bao and Dingyi Han and Yong Yu PAKDD 393-404 (2008)
to dblp by dblp on 2008-05-15 00:00:00 ]]> PAKDDconf/pakdd/2008393-404Lecture Notes in Computer ScienceSEM: Mining Spatial Events from the Web.50122008dblp 2008-05-15 00:00:00.0 Privacy-Preserving Linear Fisher Discriminant Analysis. dblphttp://www.bibsonomy.org/bibtex/27015f7d3bf5d2db22eae8c8faad3f060/dblp dblp 2008-05-15T00:00:00+02:00 dblp
Privacy-Preserving Linear Fisher Discriminant Analysis.
Shuguo Han and Wee Keong Ng PAKDD 136-147 (2008)
to dblp by dblp on 2008-05-15 00:00:00 ]]> PAKDDconf/pakdd/2008136-147Lecture Notes in Computer SciencePrivacy-Preserving Linear Fisher Discriminant Analysis.50122008dblp 2008-05-15 00:00:00.0 Pebl: web page classification without negative examples http://www.bibsonomy.org/bibtex/2fa4dd3800b3697bd7fef3ecdb0fdfae8/marcoalvarez marcoalvarez 2008-05-14T11:33:59+02:00 Classification WWW
Pebl: web page classification without negative examples
Hwanjo Yu and Jiawei Han and Kevin Chen-Chuan Chang IEEE Transactions on Knowledge and Data Engineering 16 70--81 (2004)
to Classification WWW by marcoalvarez on 2008-05-14 11:33:59 ]]> IEEE Transactions on Knowledge and Data Engineering170--81Pebl: web page classification without negative examples162004Classification WWW 2008-05-14 11:33:59.0Web page classification is one of the essential techniques for Web mining because classifying Web pages of an interesting class is often the first step of mining the Web. However, constructing a classifier for an interesting class requires laborious pre-processing such as collecting positive and negative training examples. For instance, in order to construct a �homepage� classifier, one needs to collect a sample of homepages (positive examples) and a sample of nonhomepages (negative examples). In particular, collecting negative training examples requires arduous work and caution to avoid bias. This paper presents a framework, called Positive Example Based Learning (PEBL), for Web page classification which eliminates the need for manually collecting negative training examples in preprocessing. The PEBL framework applies an algorithm, called Mapping-Convergence (M-C), to achieve high classification accuracy (with positive and unlabeled data) as high as that of a traditional SVM (with positive and negative data). M-C runs in two stages: the mapping stage and convergence stage. In the mapping stage, the algorithm uses a weak classifier that draws an initial approximation of �strong� negative data. Based on the initial approximation, the convergence stage iteratively runs an internal classifier (e.g., SVM) which maximizes margins to progressively improve the approximation of negative data. Thus, the class boundary eventually converges to the true boundary of the positive class in the feature space. We present the M-C algorithm with supporting theoretical and experimental justifications. Our experiments show that, given the same set of positive examples, the M-C algorithm outperforms one-class SVMs, and it is almost as accurate as the traditional SVMs. Data mining: an overview from a database perspective http://www.bibsonomy.org/bibtex/25e7e7afeb3b6e402b9735c27fb2fe999/marcoalvarez marcoalvarez 2008-05-14T10:25:13+02:00 DM
Data mining: an overview from a database perspective
Ming-Syan Chen and Jiawei Han and Philip S. Yu IEEE Transactions on Knowledge and Data Engineering 8 866--883 (1996)
to DM by marcoalvarez on 2008-05-14 10:25:13 ]]> IEEE Transactions on Knowledge and Data Engineering6866--883Data mining: an overview from a database perspective81996DM 2008-05-14 10:25:13.0Mining information and knowledge from large databases has been recognized by many researchers as a key research topic in database systems and machine learning, and by many industrial companies as an important area with an opportunity of major revenues. Researchers in many different fields have shown great interest in data mining. Several emerging applications in information providing services, such as data warehousing and on-line services over the Internet, also call for various data mining techniques to better understand user behavior, to improve the service provided, and to increase the business opportunities. In response to such a demand, this article is to provide a survey, from a database researcher's point of view, on the data mining techniques developed recently. A classification of the available data mining techniques is provided, and a comparative study of such techniques is presented. A framework for clustering evolving data streams http://www.bibsonomy.org/bibtex/29bc8f6c80bcae1b3cd23b8e3550e3d1e/marcoalvarez marcoalvarez 2008-05-14T10:25:13+02:00 Clustering DataStream
A framework for clustering evolving data streams
Charu C. Aggarwal and Jiawei Han and Jianyong Wang and Philip S. Yu International Conference on Very Large Data Bases 81--92 (2003)
to Clustering DataStream by marcoalvarez on 2008-05-14 10:25:13 ]]> International Conference on Very Large Data Bases81--92A framework for clustering evolving data streams2003Clustering DataStream 2008-05-14 10:25:13.0The clustering problem is a difficult problem for the data stream domain. This is because the large volumes of data arriving in a stream renders most traditional algorithms too inefficient. In recent years, a few one-pass clustering algorithms have been developed for the data stream problem. Although such methods address the scalability issues of the clustering problem, they are generally blind to the evolution of the data and do not address the following issues: (1) The quality of the clusters is poor when the data evolves considerably over time. (2) A data stream clustering algorithm requires much greater functionality in discovering and exploring clusters over different portions of the stream. The widely used practice of viewing data stream clustering algorithms as a class of one-pass clustering algorithms is not very useful from an application point of view. For example, a simple one-pass clustering algorithm over an entire data stream of a few years is dominated by the outdated history of the stream. The exploration of the stream over different time windows can provide the users with a much deeper understanding of the evolving behavior of the clusters. At the same time, it is not possible to simultaneously perform dynamic clustering over all possible time horizons for a data stream of even moderately large volume. This paper discusses a fundamentally different philosophy for data stream clustering which is guided by application-centered requirements. The idea is divide the clustering process into an online component which periodically stores detailed summary statistics and an offline component which uses only this summary statistics. The offline component is utilized by the analyst who can use a wide variety of inputs (such as time horizon or number of clusters) in order to provide a quick understanding of the broad clusters in the data stream. The problems of efficient choice, storage, and use of this statistical data for a fast data stream turns out to be quite tricky. For this purpose, we use the concepts of a pyramidal time frame in conjunction with a micro-clustering approach. Our performance experiments over a number of real and synthetic data sets illustrate the effectiveness, efficiency, and insights provided by our approach. A framework for projected clustering of high dimensional data streams http://www.bibsonomy.org/bibtex/26ff2b831e919f6ffaf05c292470c7037/marcoalvarez marcoalvarez 2008-05-14T10:25:13+02:00 Clustering DataStream
A framework for projected clustering of high dimensional data streams
Charu C. Aggarwal and Jiawei Han and Jianyong Wang and Philip S. Yu International Conference on Very Large Data Bases 852--863 (2004)
to Clustering DataStream by marcoalvarez on 2008-05-14 10:25:13 ]]> Toronto, CanadaInternational Conference on Very Large Data Bases852--863A framework for projected clustering of high dimensional data streams2004Clustering DataStream 2008-05-14 10:25:13.0The data stream problem has been studied extensively in recent years, because of the great ease in collection of stream data. The nature of stream data makes it essential to use algorithms which require only one pass over the data. Recently, single-scan, stream analiysis methods have been proposed in this context. However, a lot of stream data is high-dimensional in nature. High-dimensional data is inherently more complex in clustering, classification, and similarity search. Recent research discusses methods for projected clustering over high-dimensional data sets. This method is however difficult to generalize to data streams because of the complexity of the method and the large volume of the data streams. In this paper, we propose a new, high-dimensional, projected data stream clustering method, called HPStream. The method incorporates a fading cluster structure, and the projection based clustering methodology. It is incrementally updatable and is highly scalable on both the number of dimensions and the size of the data streams, and it achieves better clustering quality in comparison with the previous stream clustering methods. Our performance study with both real and synthetic data sets demonstrates the efficiency and efffectiveness of our proposed framework and implementation methods. Signaling Networks Involved in Retinal Degeneration in Mice. dblphttp://www.bibsonomy.org/bibtex/28745ce28434980f46721650954672077/dblp dblp 2008-05-14T00:00:00+02:00 dblp
Signaling Networks Involved in Retinal Degeneration in Mice.
Jayalakshmi Krishnan and Gwang Lee and Sang-Uk Han and Sangdun Choi FBIT 3-6 (2007)
to dblp by dblp on 2008-05-14 00:00:00 ]]> FBITconf/fbit/20073-6Signaling Networks Involved in Retinal Degeneration in Mice.2007dblp 2008-05-14 00:00:00.0