12680145
Finding Patterns in Three-Dimensional Graphs: Algorithms and Applications to Scientific Data Mining
Presents a method for finding patterns in 3D graphs. Each node in a graph is an undecomposable or atomic unit and has a label. Edges are links between the atomic units. Patterns are rigid substructures that may occur in a graph after allowing for an arbitrary number of whole-structure rotations and translations as well as a small number (specified by the user) of edit operations in the patterns or in the graph. (When a pattern appears in a graph only after the graph has been modified, we call that appearance "approximate occurrence.") The edit operations include relabeling a node, deleting a node and inserting a node. The proposed method is based on the geometric hashing technique, which hashes node-triplets of the graphs into a 3D table and compresses the label-triplets in the table. To demonstrate the utility of our algorithms, we discuss two applications of them in scientific data mining. First, we apply the method to locating frequently occurring motifs in two families of proteins pertaining to RNA-directed DNA polymerase and thymidylate synthase and use the motifs to classify the proteins. Then, we apply the method to clustering chemical compounds pertaining to aromatic compounds, bicyclicalkanes and photosynthesis. Experimental results indicate the good performance of our algorithms and high recall and precision rates for both classification and clustering.
11616193
Automatic Hierarchical Classification of Structured Deep Web Databases
We present a method that automatically classifies structured deep Web databases according to a pre-defined topic hierarchy. We assume that there are some manually classified databases, i.e., training databases, in every node of the topic hierarchy. Each training database is probed using queries constructed from the node titles of the topic hierarchy and the query result counts reported by the database are used to represent the content of the database. Hence, when adding a new database it can be probed by the same set of queries and classified to a node whose training databases are most similar to the new one. Specifically, a support vector machine classifier is trained on each internal node of the topic hierarchy with these training databases and the new database can be classified into the hierarchy top-down level by level. A feature extension method is proposed to create discriminant features. Experiments run on real structured Web databases collected from the Internet show that this classification method is quite accurate.
2694561
Identifying Approximately Common Substructures in Trees Based on a Restricted Edit Distance
In this paper we present a dynamic programming algorithm for identifying the largest approximately common substructures (LACSs) of two ordered labeled trees. We consider a substructure of a tree T to be a connected subgraph of T. Given two trees T1,T2 and an integer d, the LACS problem is to find a substructure U1 of T1 and a substructure U2 of T2 such that U1 is within distance d of U2 and where there does not exist any other substructure V1 of T1 and V2 of T2 such that V1 and V2 satisfy the distance restriction and the sum of the sizes of V1 and V2 is greater than the sum of the sizes of U1 and U2. The distance measure considered in the paper is a restricted edit distance originated from Selkow. The LACS problem is motivated by the studies of program and document comparison. The proposed algorithm solves the problem in time O(d2×|T1|×|T2|), which is as fast as the best known algorithm for calculating Selkow's distance of two trees when the distance allowed in the common substructures is a constant independent of the input trees.
2286283
Query result ranking over e-commerce web databases
To deal with the problem of too many results returned from an E-commerce Web database in response to a user query, this paper proposes a novel approach to rank the query results. Based on the user query, we speculate how much the user cares about each attribute and assign a corresponding weight to it. Then, for each tuple in the query result, each attribute value is assigned a score according to its "desirableness" to the user. These attribute value scores are combined according to the attribute weights to get a final ranking score for each tuple. Tuples with the top ranking scores are presented to the user first. Our ranking method is domain independent and requires no user feedback. Experimental results demonstrate that this ranking method can effectively capture a user's preferences.
2328843
Data-rich section extraction from HTML pages
We propose a novel algorithm, DSE (data-rich subtree extraction) to recognize and extract the data-rich section of an HTML page. We apply the DSE algorithm as a pre-processing "clean-up" step for two typical Web information retrieval problems: topic distillation and Web information extraction. Our experiments show that, for the test data sets used, the DSE algorithm can correctly identify data-rich sections of HTML pages with 100% accuracy. Therefore, it can effectively reduce the root set size for the topic distillation problem thereby improving the precision and accuracy of the IETS algorithm. Furthermore, when applied to the Web information extraction problem using the IEPAD algorithm, it can decrease the number of patterns discovered by this algorithm, thus shortening its time cost to generalize a wrapper for HTML pages.
5504923
Algorithmics and applications of tree and graph searching
Modern search engines answer keyword-based queries extremely efficiently. The impressive speed is due to clever inverted index structures, caching, a domain-independent knowledge of strings, and thousands of machines. Several research efforts have attempted to generalize keyword search to keytree and keygraph searching, because trees and graphs have many applications in next-generation database systems. This paper surveys both algorithms and applications, giving some emphasis to our own work.
61565821
Modelling and stability of FAST TCP
We introduce a discrete-time model of FAST TCP that fully captures the effect of self-clocking and compare it with the traditional continuous-time model. While the continuous-time model predicts instability for homogeneous sources sharing a single link when feedback delay is large, experiments suggest otherwise. Using the discrete-time model, we prove that FAST TCP is locally asymptotically stable in general networks when all sources have a common round-trip feedback delay, no matter how large the delay is. We also prove global stability for a single bottleneck link in the absence of feedback delay. The techniques developed here are new and applicable to other protocols.
21940463
A hybrid method for unconstrained handwritten numeral recognition by combining structural and neural "gas" classifiers
Abstract This paper presents a hybrid method for handwritten numeral recognition that combines two compensatory recognition algorithms by analysing their performance for several aspects. The skeleton-based structural recognition algorithm employed in this method is robust under distortion but sensitive to noise and flaws. On the other hand, the neural network classifier, which uses scaled binary images as features and the neural “gas” model for classification, is relatively immune to noise and flaws but sensitive to distortion. The different performances of the two algorithms for broken, connected or slanted numerals, and the measurement-level decision provided by the neural network are detected and combined with different strategies to develop matching rules for each recognition method. Five combination methods based on performance analysis are developed to meet different requirements. As the two algorithms have fairly compensatory properties, the proposed method improves the recognition rate and reliability by exploiting the advantages and avoiding the weaknesses of each classifier. The experimental results from a large set of data show the efficiency and robustness of the proposed method.
6516220
Resource-Constrained Workflow Modeling
Business processes are often constrained by resource availability. This is particularly true in an emergency response system where large quantity of resources, including emergency responders, ambulances, fire trucks, medications, food, clothing, etc., are required. In our previous work, we introduced WIFA model to meet the needs of incident command systems workflow modeling and analysis. In this paper, we extend WIFA to take resources into account when modeling and enacting workflows. A formal resource-constrained workflow model is defined and the state transitions rules are established to support workflow analysis. An example is given to illustrate the use of the model.
207320252
An efficient kernel discriminant analysis method
Small sample size and high computational complexity are two major problems encountered when traditional kernel discriminant analysis methods are applied to high-dimensional pattern classification tasks such as face recognition. In this paper, we introduce a new kernel discriminant learning method, which is able to effectively address the two problems by using regularization and subspace decomposition techniques. Experiments performed on real face databases indicate that the proposed method outperforms, in terms of classification accuracy, existing kernel methods, such as kernel principal component analysis and kernel linear discriminant analysis, at a significantly reduced computational cost.
2749844
Image and Video Segmentation by Anisotropic Kernel Mean Shift
Mean shift is a nonparametric estimator of density which has been applied to image and video segmentation. Traditional mean shift based segmentation uses a radially symmetric kernel to estimate local density, which is not optimal in view of the often structured nature of image and more particularly video data. In this paper we present an anisotropic kernel mean shift in which the shape, scale, and orientation of the kernels adapt to the local structure of the image or video. We decompose the anisotropic kernel to provide handles for modifying the segmentation based on simple heuristics. Experimental results show that the anisotropic kernel mean shift outperforms the original mean shift on image and video segmentation in the following aspects: 1) it gets better results on general images and video in a smoothness sense; 2) the segmented results are more consistent with human visual saliency; 3) the algorithm is robust to initial parameters.