46154955
A Novel Typical-Sample-Weighted Clustering Algorithm for Large Data Sets
In the field of cluster analysis, most of existing algorithms are developed for small data sets, which cannot effectively process the large data sets encountered in data mining. Moreover, most clustering algorithms consider the contribution of each sample for classification uniformly. In fact, different samples should be of different contribution for clustering result. For this purpose, a novel typical-sample-weighted clustering algorithm is proposed for large data sets. By the atom clustering, the new algorithm extracts the typical samples to reduce the data amount. Then the extracted samples are weighted by their corresponding typicality and then clustered by the classical fuzzy c-means (FCM) algorithm. Finally, the Mahalanobis distance is employed to classify each original sample into obtained clusters. It is obvious that the novel algorithm can improve the speed and robustness of the traditional FCM algorithm. The experimental results with various test data sets illustrate the effectiveness of the proposed clustering algorithm.
8123307
Strategies anticipating a difference in search depth using opponent-model search
In this contribution we propose a class of strategies which focus on the game as well as on the opponent. Preference is given to the thoughts of the opponent, so that the strategy under investigation might be speculative. We describe a generalization of OM search, called (D, d)-OM search, where D stands for the depth of search by the player and d for the opponent's depth of search. A known difference in search depth can be exploited by purposely choosing a suboptimal variation with the aim to gain a larger advantage than when playing the objectively best move. The difference in search depth may have the result that the opponent does not see the variation in sufficiently deep detail. We then give a pruning alternative for (D, d)-OM search, denoted by α-β 2 pruning. A best-case analysis shows that α-β 2 prunes very efficiently, comparable to the efficiency of α-β with regard to minimax. The effectiveness of the proposed strategy is confirmed by simulations using a game-tree model including an opponent model and by experiments in the domain of Othello.
2376280
A Radar Target Multi-feature Fusion Classifier Based on Rough Neural Network
Fusing Multi-feature will benefit radar target classification with much more belief. However, since radar target attributes such as high range resolution profiles, waveforms, frequency spectra, time-frequency spectra, higher order statistics, polarization spectra and flight path are of different dimensions, it is hard to make decision by fusing multi-feature directly. Fortunately, rough set makes decision by examining the fitness of each condition attribute separately, while neural network is powerful for dealing with nonlinear problems. With radial projection of target dimension, cruising velocity and height as condition attribute, a multi-feature rough neural network fusion classifier is presented. Simulation of the proposed classifier based on an information system with 25 targets belonging to 6 classes shows accuracy not less than 93%, while attributes are subjoined with typical radar errors.
35059796
A Unified Framework for Shot Boundary Detection
According to drawbacks of available algorithms,a new hierarchical and multiresolution approach to the detection and classification of scene breaks in video sequences is presented in this paper. This method gives a unified framework for different shots by using the multi-resolution analysis as mainstay. Firstly, the video clips are cut by FCM clustering method, then the fade and dissolve are detected using ECR and SCD algorithm in the high components and the low components respectively by integer-to-integer wavelet transform. Finally, according to the cut and the former detection of gradual changes, we use motion vectors in high-components of 3D-WT to detect the wipe transition. Experimental results with real video clips about 7h demonstrate that our method can detect and classify a variety of scene breaks, including cuts, fades and dissolves, even in sequences involving significant motions and flash.
11815466
A Wavelet Based Image Watermarking Scheme
A novel robust watermarking scheme based on wavelet transform is presented in this paper. The watermark is embedded into significant triplet of wavelet coefficients in the three lowest detail subband images. The embedding algorithm only needs to modify a small amount of the wavelet coefficients, which leads to very small distortion and guarantees the visual quality of the watermarked image. The performance of the proposed algorithm is tested on a series of normal attacks in image processing, such as JPEG compression, Gaussian noise addition, median filter, etc. The corresponding experimental results show that the proposed watermarking scheme has better performance.
7186941
A Cartoon Video Detection Method Based on Active Relevance Feedback and SVM
By analyzing the particular features of visual content for cartoon videos, 8 typical features of MPEG-7 descriptors are extracted to distinguish the cartoons from other videos. Then, a content-based video classifier is developed by combining the active relevance feedback technique and SVM for detecting the cartoon videos. The experimental results on the vast real video clips illustrate that compared with the classifier based on SVM and that based on traditional relevance feedback technique and SVM, the proposed classifier has a higher advantage of cartoon video detection.
10271659
Gene2Oligo: oligonucleotide design for in vitro gene synthesis
There is substantial interest in implementing a bioinformatics tool that allows the design of oligonucleotides to support the development of in vitro gene synthesis. Current protocols to make long synthetic DNA molecules rely on the in vitro assembly of a set of short oligonucleotides, either by ligase chain reaction (LCR) or by assembly PCR. Ideally, such oligonucleotides should represent both strands of the final DNA molecule. They should be adjacent on the same strand and overlap the complementary oligonucleotides from the second strand to ensure good hybridization during assembly. This implies that the thermodynamic properties of each oligonucleotide have to be consistent across the set. Furthermore, any given oligonucleotide has to be totally specific to its target to avoid the creation of incorrectly assembled sequences. We have developed Gene2Oligo (http://berry.engin.umich.edu/gene2oligo/), a web-based tool that divides a long input DNA sequence into a set of adjacent oligonucleotides representing both DNA strands. The length of the oligonucleotides is dynamically optimized to ensure both the specificity and the uniform melting temperatures necessary for in vitro gene synthesis. We have successfully designed and used a set of oligonucleotides to synthesize the Saccharomyces cerevisiae cytochrome b5 by using both LCR and assembly PCR.
13249479
Fair and efficient router congestion control
Congestion is a natural phenomenon in any network queuing system, and is unavoidable if the queuing system is operated near capacity. In this paper we study how to set the rules of a queuing system so that all the users have a self-interest in controlling congestion when it happens.Routers in the internet respond to local congestion by dropping packets. But if packets are dropped indiscriminately, the effect can be to encourage senders to actually increase their transmission rates, worsening the congestion and destabilizing the system. Alternatively, and only slightly more preferably, the effect can be to arbitrarily let a few insistent senders take over most of the router capacity.We approach this problem from first principles: a router packet-dropping protocol is a mechanism that sets up a game between the senders, who are in turn competing for link capacity. Our task is to design this mechanism so that the game equilibrium is desirable: high total rate is achieved and is shared widely among all senders. In addition, equilibrium should be reestablished quickly in response to changes in transmission rates. Our solution is based upon auction theory: in principle, although not always in practice, we drop packets of the highest-rate sender, in case of congestion. We will prove the game-theoretic merits of our method. We'll also describe a variant of the method with some further advantages that will be supported by network simulations.
1168192
Approximately Repetitive Structure Detection for Wrapper Induction
In recent years, much work has been invested into automatically learning wrappers for information extraction from HTML tables and lists. Our research has focused on a system that can learn a wrapper from a single unlabelled page. An essential step is to locate the tabular data within the page. This is not trivial when the structures of data tuples are similar but not identical. In this paper we describe an algorithm that can automatically detect approximate repetitive structures within one sequence. The algorithm does not rely on any domain knowledge or HTML heuristics and it can be used in detecting repetitive patterns and hence to learn wrappers from a single unlabeled tabular page.
17022495
Improving Web clustering by cluster selection
Web page clustering is a technology that puts semantically related Web pages into groups and is useful for categorizing, organizing, and refining search results. When clustering using only textual information, suffix tree clustering (STC) outperforms other clustering algorithms by making use of phrases and allowing clusters to overlap. One problem of STC and other similar algorithms is how to select a small set of clusters to display to the user from a very large set of generated clusters. The cluster selection method used in STC is flawed in that it does not handle overlapping clusters appropriately. This paper introduces a new cluster scoring function and a new cluster selection algorithm to overcome the problems with overlapping clusters, which are combined with STC to make a new clustering algorithm ESTC. This paper's experiments show that ESTC significantly outperforms STC and that even with less data ESTC performs similarly to a commercial clustering search engine.
14110256
Query Directed Web Page Clustering
Web page clustering methods categorize and organize search results into semantically meaningful clusters that assist users with search refinement; but finding clusters that are semantically meaningful to users is difficult. In this paper, we describe a new Web page clustering algorithm, QDC, which uses the user's query as part of a reliable measure of cluster quality. The new algorithm has five key innovations: a new query directed cluster quality guide that uses the relationship between clusters and the query, an improved cluster merging method that generates semantically coherent clusters by using cluster description similarity in additional to cluster overlap, a new cluster splitting method that fixes the cluster chaining or cluster drifting problem, an improved heuristic for cluster selection that uses the query directed cluster quality guide, and a new method of improving clusters by ranking the pages by relevance to the cluster. We evaluate QDC by comparing its clustering performance against that of four other algorithms on eight data sets (four use full text data and four use snippet data) by using eleven different external evaluation measurements. We also evaluate QDC by informally analysing its real world usability and performance through comparison with six other algorithms on four data sets. QDC provides a substantial performance improvement over other Web page clustering algorithms