40737527
On the computational complexity of edge concentration
Suppose that G=(U,L,E) is a bipartite graph with vertex set U∪L and edge set E⊆U×L. A typical convention for drawing G is to put the vertices of U on a horizontal line and the vertices of L on another horizontal line, and then to represent edges by line segments between the vertices that determine them. “Edge concentration” is known as an effective method to draw dense bipartite graphs clearly. The key in the edge concentration method is to reduce the number of edges, while the graph structural information is retained. The problem of having a maximal reduction on the number of edges by the edge concentration method was left open. In this paper we show that this problem is NP-hard.
17501980
Term Graph Model for Text Classification
Most existing text classification methods (and text mining methods at large) are based on representing the documents using the traditional vector space model. We argue that important information, such as the relationship among words, is lost. We propose a term graph model to represent not only the content of a document but also the relationship among the keywords. We demonstrate that the new model enables us to define new similarity functions, such as considering rank correlation based on PageRank-style algorithms, for the classification purpose. Our preliminary results show promising results of our new model.
14038708
Efficiently computing frequent tree-like topology patterns in a Web environment
Data mining has recently emerged in a number of real life applications, such as banking, supermarketing, and interneting. The authors investigate a novel data mining problem derived from the applications of WWW. They present an efficient algorithm for solving the problem. The initial implementation suggests that the algorithm is very efficient and scalable in practice.
6221918
Efficiently Matching Proximity Relationships in Spatial Databases
Spatial data mining recently emerges from a number of real applications, such as real-estate marketing, urban planning, weather forecasting, medical image analysis, road traffic accident analysis, etc. It demands for efficient solutions for many new, expensive, and complicated problems. In this paper, we investigate a proximity matching problem among clusters and features. The investigation involves proximity relationship measurement between clusters and features. We measure proximity in an average fashion to address possible nonuniform data distribution in a cluster. An efficient algorithm, for solving the problem, is proposed and evaluated. The algorithm applies a standard multistep paradigm in combining with novel lower and upper proximity bounds. The algorithm is implemented in several different modes. Our experiment results do not only give a comparison among them but also illustrate the efficiency of the algorithm.
4018890
Clustering Moving Objects for Spatio-temporal Selectivity Estimation
Many spatio-temporal applications involve managing and querying moving objects. In such an environment, predictive spatio-temporal queries become an important query class to be processed to capture the nature of moving objects. In this paper, we investigated the problem of selectivity estimation for predictive spatio-temporal queries. We propose a novel histogram technique based on a clustering paradigm. To avoid expensive computation costs, we developed linear time heuristics to construct such a histogram. Our performance study indicated that the new techniques improve the accuracy of the existing techniques by one order of magnitude.
1993887
Locating Motifs in Time-Series Data
Finding motifs in time-series is proposed to make clustering of time-series subsequences meaningful, because most existing algorithms of clustering time-series subsequences are reported meaningless in recent studies. The existing motif finding algorithms emphasize the efficiency at the expense of quality, in terms of the number of time-series subsequences in a motif and the total number of motifs found. In this paper, we formalize the problem as a continuous top-k motif balls problem in an m-dimensional space, and propose heuristic approaches that can significantly improve the quality of motifs with reasonable overhead, as shown in our experimental studies.
27372511
A comprehensive image processing suite for book re-mastering
Converting paper books into electronic form provides benefits for archiving, distribution and content reuse. However, directly scanned images are usually undesirable for electronic books, and automated content re-mastering is required. In this paper, we describe a comprehensive image processing suite consisting of three major components: 1) image enhancement with deskew, cropping, color correction, contrast enhancement and text sharpening, 2) compound document image compression, and 3) extraction of TOC (table of content) and linking. We built a processing pipeline that automatically converts a set of scanned page images into a high quality and highly compressed e-book in the popular PDF format.
5163377
Handwritten numeral recognition using MFNN based multiexpert combination strategy
A novel unconstrained numeral recognition system using an MFNN (multilayered feedforward neural net)-based multi-expert combination strategy is proposed. Taking advantage of every method's confidence value as well as its output label, this strategy can improve the system's performance significantly with as few as two experts. Another novel feature of the system is that the multi-expert combination problem is converted into a new classification problem, and then the MFNNs are used to handle the combination adaptively. The proposed system has achieved promising results on the NIST database.
26474180
Adaptive confidence transform based classifier combination for Chinese character recognition
Abstract Classifier combination is an effective way to improve recognition performance. However, in Chinese character recognition the extremely large number of categories results in several difficulties for the combination. In order to overcome these difficulties a novel combination method is presented in this paper. It consists of three main components: adaptive confidence transform (ACT), consensus theoretic combination and reliability based speedup scheme. ACT, which can estimate a posteriori probabilities from raw measurement values, is the focus of this paper. Experimental results show a significant reduction of error rates in both printed (PCCR) and handwritten Chinese character recognition (HCCR).
503545
Privacy-preserving clustering with distributed EM mixture modeling
Privacy and security considerations can prevent sharing of data, derailing data mining projects. Distributed knowledge discovery can alleviate this problem. We present a technique that uses EM mixture modeling to perform clustering on distributed data. This method controls data sharing, preventing disclosure of individual data items or any results that can be traced to an individual site.
1863895
Low-Complexity and Distributed Energy Minimization in Multihop Wireless Networks
In this work, we study the problem of minimizing the total power consumption in a multihop wireless network subject to a given offered load. It is well-known that the total power consumption of multihop wireless networks can be substantially reduced by jointly optimizing power control, link scheduling, and routing. However, the known optimal cross-layer solution to this problem is centralized and with high computational complexity. In this paper, we develop a low-complexity and distributed algorithm that is provably power-efficient. In particular, under the node-exclusive interference model and with suitable assumptions on the power-rate function, we can show that the total power consumption of our algorithm is at most (2+¿) times as large as the power consumption of the optimal (but centralized and complex) algorithm, where ¿ is an arbitrarily small positive constant. Our algorithm is not only the first such distributed solution with provable performance bound, but its power-efficiency ratio is also tighter than that of another suboptimal centralized algorithm in the literature.