21957858
Algorithms for Approximate Graph Matching
Abstract Labeled graphs are graphs in which each node and edge has a label. The distance between two labeled graphs is considered to be the weighted sum of the costs of edit operations (insert, delete, and relabel the nodes and edges) to transform one graph to the other. The paper considers two variants of the approximate graph matching problem (AGM): Given a pattern graph P and a data graph D : 1. 1. What is the distance between P and D ? 2. 2. What is the minimum distance between P and D when subgraphs can be freely removed from D ? We first show that no efficient algorithm can solve either variant of the AGM unless P = NP. Then we present several heuristic algorithms leading to approximate solutions. The heuristics are based on probabilistic hill climbing and maximum flow techniques. Our experimental results involving the comparison of real and simulated data demonstrate the good performance of the algorithms presented.
14900181
Unordered tree mining with applications to phylogeny
Frequent structure mining (FSM) aims to discover and extract patterns frequently occurring in structural data, such as trees and graphs. FSM finds many applications in bioinformatics, XML processing, Web log analysis, and so on. We present a new FSM technique for finding patterns in rooted unordered labeled trees. The patterns of interest are cousin pairs in these trees. A cousin pair is a pair of nodes sharing the same parent, the same grandparent, or the same great-grandparent, etc. Given a tree T, our algorithm finds all interesting cousin pairs of T in O(|T|/sup 2/) time where |T| is the number of nodes in T. Experimental results on synthetic data and phylogenies show the scalability and effectiveness of the proposed technique. To demonstrate the usefulness of our approach, we discuss its applications to locating co-occurring patterns in multiple evolutionary trees, evaluating the consensus of equally parsimonious trees, and finding kernel trees of groups of phylogenies. We also describe extensions of our algorithms for undirected acyclic graphs (or free trees).
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.
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.
236203
New Techniques for DNA Sequence Classification
DNA sequence classification is the activity of determining whether or not an unlabeled sequence S belongs to an existing class C. This paper proposes two new techniques for DNA sequence classification. The first technique works by comparing the unlabeled sequence S with a group of active motifs discovered from the elements of C and by distinction with elements outside of C. The second technique generates and matches gapped fingerprints of S with elements of C. Experimental results obtained by running these algorithms on long and well conserved Alu sequences demonstrate the good performance of the presented methods compared with FASTA. When applied to less conserved and relatively short functional sites such as splice-junctions, a variation of the second technique combining fingerprinting with consensus sequence analysis gives better results than the current classifiers employing text compression and machine learning algorithms.
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.
44970162
Toward Web mining of cross-language query translations in digital libraries
This paper proposes an effective query-translation approach that enables a cross-language information retrieval (CLIR) service to be more easily supported in digital library systems that only contain monolingual content. A query-translation engine called LiveTrans is used to process the translation requests of cross-lingual queries from connected digital library systems. To automatically extract translations not covered by standard dictionaries, the engine is developed based on a novel integration of dictionary resources and Web mining approaches, including anchor-text and search-result methods. The engine exploits a broad range of multilingual Web resources used as live bilingual corpora to alleviate translation difficulties. It is shown to be particularly effective for extracting multilingual translation equivalents of query terms containing proper names or new terminology. The obtained results show the feasibility of and great potential for creating English-Chinese CLIR services in existing digital libraries and new applications in cross-language Web searching, although difficulties still remain that need to be investigated further.
38052408
Towards a practical, secure, and very large scale online election
We propose a practical and secure electronic voting protocol for large-scale online elections. Our protocol satisfies a large set of important criteria that has never been put together in a single protocol before. Among all electronic voting schemes in the literature, Sensus, a security-conscious electronic voting protocol proposed by Cranor and Cytron (1997), satisfies most of our criteria. Sensus has been implemented and used in mock elections. However, Sensus suffers from several major drawbacks. For instance, we show that even if all voters follow the Sensus protocol honestly, some voters' votes may still be replaced with different votes without being detected. Our protocol overcomes these drawbacks.
2685835
Energy Consumption of Swarm Intelligence inspired Routing Algorithms in MANETs
Swarm Intelligence (SI) inspired routing algorithms have become a research focus in recent years due to their self-organizing nature, which is very suitable to the routing problems in Mobile Ad hoc Networks (MANETs). The network information can be collected and updated in a decentralized and dynamic way through the localized cooperation among these swarm agents. In this paper, we, for the first time, provide a comprehensive comparison about current SI inspired routing algorithms in MANETs in the aspects of network metrics and simulation environment. Then, we make a detailed study about the energy consumption of the SI inspired routing problem in MANETs. Based on this study, we present the selection criterion of the intermediate nodes so as to be energy efficient. Experimental results are provided with reasonable verification.
17015567
Effect of channel-estimation error on QAM systems with antenna diversity
This paper studies the effect of channel estimation error and antenna diversity on multilevel quadrature amplitude modulation (M-QAM) systems over Rayleigh fading channels. Based on the characteristic function method, a general closed-form bit-error rate (BER) for M-QAM systems is presented. The effect of the inaccurate channel estimation on the performance for pilot-symbol-assisted modulation M-QAM systems with antenna diversity is investigated. Simulation results for M-QAM (M = 4, 16, 64, 256, etc.) show that the analytical method can accurately estimate the system performance. Moreover, numerical results show that with the antenna-diversity technique, the BER performance improves significantly, especially in perfect channel-estimation cases. It is also found that the channel-estimation error limits the benefit of antenna diversity. By increasing the length of the channel estimator and the amplitude of the pilot symbol, more accurate channel estimation can be achieved, so that the BER performance is improved.
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.