7014119
Discovering Relational Patterns across Multiple Databases
Relational patterns across multiple databases can reveal special pattern relationships hidden inside data collections. Existing research in data mining has made significant efforts in discovering different types of patterns from single or multiple databases, but how to find patterns that have a higher support in database A than in database B with a given support threshold a is still an open problem. We propose in this paper DRAMA, a systematic framework for discovering relational patterns across multiple databases. More specifically, given a series of data collections, we try to discover patterns from different databases with patterns' relationships satisfying the user specified constraints. Our method seeks to build a hybrid frequent pattern tree (HFP-tree) from multiple databases, and mine patterns from the HFP-tree by integrating users' constraints into the pattern mining process.
15136742
Mining Complex Patterns across Sequences with Gap Requirements
The recurring appearance of sequential patterns, when confined by the predefined gap requirements, often implies strong temporal correlations or trends among pattern elements. In this paper, we study the problem of mining a set of gap constrained sequential patterns across multiple sequences. Given a set of sequences S1, S2,., SK constituting a single hypersequence S, we aim to find recurring patterns in S, say P, which may cross multiple sequences with all their matching characters in S bounded by the user specified gap constraints. Because of the combinatorial candidate explosion, traditional Apriori-based algorithms are computationally infeasible. Our research proposes a new mechanism to ensure pattern growing and pruning. When combining the pruning technique with our Gap Constrained Search (GCS) and map-based support prediction approaches, our method achieves a speed about 40 times faster than its other peers.
5621999
Accelerated Kernel Feature Analysis
A fast algorithm, Accelerated Kernel Feature Analysis (AKFA), that discovers salient features evidenced in a sample of n unclassified patterns, is presented. Like earlier kernel-based feature selection algorithms, AKFA implicitly embeds each pattern into a Hilbert space, H, induced by a Mercer kernel. An \ell-dimensional linear subspace of H is iteratively constructed by maximizing a variance condition for the nonlinearly transformed sample. This linear subspace can then be used to define more efficient data representations and pattern classifiers. AKFA requires O(\elln2) operations, as compared to 0(n^3) for Sch¨olkof, Smola, and M¨uller’s Kernel Principal Component Analysis (KPCA), and O(\ell^2 n^2) for Smola, Mangasarian, and Sch¨olkopf’s Sparse Kernel Feature Analysis (SKFA). Numerical experiments show that AKFA can generate more concise feature representations than both KPCA and SKFA, and demonstrate that AKFA obtains similar classification performance as KPCA for a face recognition problem.
14899560
Dealing with Predictive-but-Unpredictable Attributes in Noisy Data Sources
Attribute noise can affect classification learning. Previous work in handling attribute noise has focused on those predictable attributes that can be predicted by the class and other attributes. However, attributes can often be predictive but unpredictable. Being predictive, they are essential to classification learning and it is important to handle their noise. Being unpredictable, they require strategies different from those of predictable attributes. This paper presents a study on identifying, cleansing and measuring noise for predictive-but-unpredictable attributes. New strategies are accordingly proposed. Both theoretical analysis and empirical evidence suggest that these strategies are more effective and more efficient than previous alternatives.
11232317
Cost-constrained data acquisition for intelligent data preparation
Real-world data is noisy and can often suffer from corruptions or incomplete values that may impact the models created from the data. To build accurate predictive models, data acquisition is usually adopted to prepare the data and complete missing values. However, due to the significant cost of doing so and the inherent correlations in the data set, acquiring correct information for all instances is prohibitive and unnecessary. An interesting and important problem that arises here is to select what kinds of instances to complete so the model built from the processed data can receive the "maximum" performance improvement. This problem is complicated by the reality that the costs associated with the attributes are different, and fixing the missing values of some attributes is inherently more expensive than others. Therefore, the problem becomes that given a fixed budget, what kinds of instances should be selected for preparation, so that the learner built from the processed data set can maximize its performance? In this paper, we propose a solution for this problem, and the essential idea is to combine attribute costs and the relevance of each attribute to the target concept, so that the data acquisition can pay more attention to those attributes that are cheap in price but informative for classification. To this end, we will first introduce a unique economical factor (EF) that seamlessly integrates the cost and the importance (in terms of classification) of each attribute. Then, we will propose a cost-constrained data acquisition model, where active learning, missing value prediction, and impact-sensitive instance ranking are combined for effective data acquisition. Experimental results and comparative studies from real-world data sets demonstrate the effectiveness of our method.
3813357
Video data mining: semantic indexing and event detection from the association perspective
Advances in the media and entertainment industries, including streaming audio and digital TV, present new challenges for managing and accessing large audio-visual collections. Current content management systems support retrieval using low-level features, such as motion, color, and texture. However, low-level features often have little meaning for naive users, who much prefer to identify content using high-level semantics or concepts. This creates a gap between systems and their users that must be bridged for these systems to be used effectively. To this end, in this paper, we first present a knowledge-based video indexing and content management framework for domain specific videos (using basketball video as an example). We will provide a solution to explore video knowledge by mining associations from video data. The explicit definitions and evaluation measures (e.g., temporal support and confidence) for video associations are proposed by integrating the distinct feature of video data. Our approach uses video processing techniques to find visual and audio cues (e.g., court field, camera motion activities, and applause), introduces multilevel sequential association mining to explore associations among the audio and visual cues, classifies the associations by assigning each of them with a class label, and uses their appearances in the video to construct video indices. Our experimental results demonstrate the performance of the proposed approach.
20473974
4-Colorable 6-regular toroidal graphs
This paper proves two conjectures of Collins, Fisher and Hutchinson about the chromatic number of some circulant graphs. As a consequence, we characterize 4-colorable 6-regular toroidal graphs.
10041607
Comparison of Modularization Methods in Application to Different Biological Networks
Most biological networks have been proposed to possess modular organization, which increases the robustness, flexibility, and stability of networks. Many clustering methods have been used in mining biological data and partitioning complex networks into functional modules. Most of these methods require presetting the number of modules and therefore can potentially obtain biased results. The Markov clustering method (MCL) and the simulated annealing module-detection method (SA) eliminate this requirement and can objectively separate relatively dense subgraphs. In this paper, we compared these two module-detection methods for three types of biological data: protein family classification, microarray clustering, and modularity of metabolic networks. We found that these two methods show differential advantages for different biological networks. In the case of the gene network based on Affymetrix microarray spike data, MCL exactly identified the same number of groups and same contents in each group set by the spike data. In the case of the gene network derived from actual expression data, although neither of the two methods can perfectly recover the natural classification, MCL performs slightly better than SA. However, with increased random noise added to the gene expression values, SA generates better modular structures with higher modularity. Next we compared the modularization results of MCL and SA for protein family classification and found the modules detected by SA could not be well matched with the Structural Classification of Proteins (SCOP database), which suggests that MCL is ideally suited to the rapid and accurate detection of protein families. In addition, we used both methods to detect modules in the metabolic network of E. coli. MCL gives a trivial clustering, which generates biologically insignificant modules. In contrast, SA detects modules well corresponding to the KEGG functional classification. Moreover the modularity for several other metabolic networks detected by SA is also much higher than that by MCL. In summary, MCL is more suited to modularize relatively complete and definite data, such as a protein family network. In contrast, SA is less sensitive to noise such as experimental error or incomplete data and outperforms MCL when modularizing gene networks based on microarray data and large scale metabolic networks constructed from incomplete databases.
11848222
Asymptotic Clique Covering Ratios of Distance Graphs
Given a finite set D of positive integers, the distance graph G(Z, D) has Z as the vertex set and { ij: | i?j | ?D } as the edge set. Given D, the asymptotic clique covering ratio is defined as S(D) =lim supn?∞ncl(n), where cl(n) is the minimum number of cliques covering any consecutive n vertices of G(Z, D). The parameter S(D) is closely related to the ratio spT(G)?(G)of a graph G, where ?(G) and spT(G) denote, respectively, the chromatic number and the optimal span of a T -coloring of G. We prove that for any finite set D, S(D) is a rational number and can be realized by a `periodical? clique covering of G(Z, D). Then we investigate the problem for which sets D the equality S(D) =?(G(Z, D)) holds. (In general, S(D) ??(G(Z, D)), where ?(G) is the clique number of G.) This problem turns out to be related to T -colorings and to fractional chromatic number and circular chromatic number of distance graphs. Through such connections, we shall show that the equality S(D) =?(G(Z, D)) holds for many classes of distance graphs. Moreover, we raise questions regarding other such connections.
52810083
A hierarchical modeling framework for on-chip communication architectures
The communication sub-system of complex IC systems is increasingly critical for achieving system performance. Given this, it is important that the on-chip communication architecture should be included in any quantitative evaluation of system design during design space exploration. While there are several mature methodologies for the modeling and evaluation of architectures of processing elements, there is relatively little work done in modeling of an extensive range of on-chip communication architectures, and integrating this into a single modeling and simulation environment combining processing element and on-chip communication architectures. This paper describes a modeling framework with accompanying simulation tools that attempts to fill this gap. Based on an analysis of a wide range of on-chip communication architectures, we describe how a specific hierarchical class library can be used to develop new on-chip communication architectures, or variants of existing ones with relatively little incremental effort. We demonstrate this through three case studies including two commercial on-chip bus systems and an on-chip packet switching network. Here we show that through careful analysis and construction it is possible for the modeling environment to support the common features of these architectures as part of the library and permit instantiation of the individual architectures as variants of the library design. As part of this methodology we also show how different levels of abstraction of the model can be supported and viewed as different variants that can be used in an accuracy versus simulation time trade-off.
12133267
A note on graph reconstruction
Suppose G and G′ are graphs on the same vertex set V such that for each x ∈ V there is an isomorphism θx of G−x to G′−x. We prove in this paper that if there is a vertex x ∈ V and an automorphism σ of G − x such that θx agrees with σ on all except for at most three vertices of V −x, then G is isomorphic to G′. As a corollary we prove that if a graph G has a vertex which is contained in at most three bad pairs, then G is reconstructible. Here a pair of vertices x, y of a graph G is called a bad pair if there exist u, v ∈ V (G) such that {u, v} 6 = {x, y} and G− {x, y} is isomorphic to G− {u, v}. All graphs discussed here are finite simple graphs. The vertex set and edge set of a graph G are denoted by V (G) and E(G) respectively. If A is a subset of V (G), we use G|A and G−A to denote the subgraphs of G induced by A and V (G)−A respectively. When A = {x} is a singleton, we use G−x instead of G−{x}. For two subsets X, Y of V (G), we use eG(X, Y ) to denote the number of edges joining a vertex of X to a vertex of Y . For brevity, we write eG(x, X) for eG({x}, X) and eG(X) for eG(X, X). The degree of x in G is denoted by dG(x). We shall use some notations defined in [4]. Two graphs G and H are hypomorphic if there exists a bijection f : V (G) → V (H) such that G− x is ∗Current address: SFB 343, Universität Bielefeld, D-33501 Bielefeld, FRG