15536346
An Index Structure for Data Mining and Clustering
Abstract. In this paper we present an index structure, called MetricMap, that takes a set of objects and a distance metric and then maps those objects to a k-dimensional space in such a way that the distances among objects are approximately preserved. The index structure is a useful tool for clustering and visualization in data-intensive applications, because it replaces expensive distance calculations by sum-of-square calculations. This can make clustering in large databases with expensive distance metrics practical. We compare the index structure with another data mining index structure, FastMap, recently proposed by Faloutsos and Lin, according to two criteria: relative error and clustering accuracy. For relative error, we show that (i) FastMap gives a lower relative error than MetricMap for Euclidean distances, (ii) MetricMap gives a lower relative error than FastMap for non-Euclidean distances (i.e., general distance metrics), and (iii) combining the two reduces the error yet further. A similar result is obtained when comparing the accuracy of clustering. These results hold for different data sizes. The main qualitative conclusion is that these two index structures capture complementary information about distance metrics and therefore can be used together to great benefit. The net effect is that multi-day computations can be done in minutes.
11135944
Fuzzy Clustering of Web Logs for User Classification
7539391
Compressed-domain video indexing techniques using DCT and motion vector information in MPEG video
Development of various multimedia applications hinges on the availability of fast and efficient storage, browsing, indexing, and retrieval techniques. Given that video is typically stored efficiently in a compressed format, if we can analyze the compressed representation directly, we can avoid the costly overhead of decompressing and operating at the pixel level. Compressed domain parsing of video has been presented in earlier work where a video clip is divided into shots, subshots, and scenes. In this paper, we describe key frame selection, feature extraction, and indexing and retrieval techniques that are directly applicable to MPEG compressed video. We develop a frame-type independent representation of the various types of frames present in an MPEG video in which al frames can be considered equivalent. Features are derived from the available DCT, macroblock, and motion vector information and mapped to a low-dimensional space where they can be accessed with standard database techniques. The spatial information is used as primary index while the temporal information is used to enhance the robustness of the system during the retrieval process. The techniques presented enable fast archiving, indexing, and retrieval of video. Our operational prototype typically takes a fraction of a second to retrieve similar video scenes from our database, with over 95% success.
17442336
Ordinal association rules for error identification in data sets
A new extension of the Boolean association rules, ordinal association rules, that incorporates ordinal relationships among data items, is introduced. One use for ordinal rules is to identify possible errors in data. A method that finds these rules and identifies potential errors in data is proposed.
46019766
A Study on Textual Entailment
In this paper we study a graph-based approach to the task of textual entailment between a text and hypothesis. The approach takes into account the full lexico-syntactic context of both the text and hypothesis and relies heavily on the concept of subsumption. It starts with mapping the text and hypothesis into graph structures where nodes represent concepts and edges represent lexico-syntactic relations among concepts. Based on a subsumption score between the text-graph and hypothesis-graph an entailment decision is then made. The impact of synonymy on entailment is quantified and discussed. An important advantage of our solution is the ability to customize it so that high-confidence results are obtained
18594508
An index structure for efficient reverse nearest neighbor queries
The Reverse Nearest Neighbor (RNN) problem is to find all points in a given data set whose nearest neighbor is a given query point. Just like the Nearest Neighbor (NN) queries, the RNN queries appear in many practical situations such as marketing and resource management. Thus, efficient methods for the RNN queries in databases are required. The paper introduces a new index structure, the Rdnn-tree, that answers both RNN and NN queries efficiently. A single index structure is employed for a dynamic database, in contrast to the use of multiple indexes in previous work. This leads to significant savings in dynamically maintaining the index structure. The Rdnn-tree outperforms existing methods in various aspects. Experiments on both synthetic and real world data show that our index structure outperforms previous methods by a significant margin (more than 90% in terms of number of leaf nodes accessed) in RNN queries. It also shows improvement in NN queries over standard techniques. Furthermore, performance in insertion and deletion is significantly enhanced by the ability to combine multiple queries (NN and RNN) in one traversal of the tree. These facts make our index structure extremely preferable in both static and dynamic cases.
13196530
Amino Acid Substitution Matrices from an Artificial Neural Network Model
An amino acid substitution matrix specifies probabilities of substitutions for each pair of the 20 amino acids. Log-odds scores transformed from the values in substitution matrices are widely used to construct protein sequence alignments. Any given substitution matrix is suited to matching sequences diverged by a specific evolutionary distance. However, for a given set of sequences, it is not always clear what matrix should be used. We used an artificial neural network model to predict probabilities of amino acid substitutions with alignment samples of different evolutionary distances. From this internal description, substitution matrices suitable for detecting relationships at any chosen evolutionary distance can be instantly generated. By using the additional information of evolutionary distances, the average cross entropy error of our neural network model is lower than that of a series of BLOSUM and PET matrices over all testing sets. Our model is more accurate on the prediction of amino acid substitution probabilities.
9933111
Service-oriented computing
Service-oriented computing using Web services has emerged as a major research topic in recent years. Strong support from major computer companies including IBM, Microsoft, Hewlett-Packard, Oracle, and SAP has accelerated the acceptance and adoption of SOC using Web services. With SOC, enterprises can define and execute transactions across multiple business. Companies can use SOC's plug-and-play interoperability to compose business processes and integrate different information systems on the fly to enable ad hoc cooperation between new partners. Thus, SOC reinvents the way enterprises work together: common tasks in a business process or supply chain can be easily outsourced to external service providers for both performance and cost reasons.
10903877
WISE-building simple intelligence into Web services
Web services are self contained and self described modular applications that can be published, discovered and employed on the Web. Many standard protocols supporting Web services have been adopted and more are being proposed. We study the issue on providing intelligent Web services. We propose the enhancement of Web service functionalities by deploying software agents on both server side and/or client side. Our goal of designing the WISE Web service architecture is to provide a working middle ground between the current Web service standards and the semantic Web architecture. The WISE software agent architecture for Web services is presented. We discuss the design issues of WISE. We also present the QoS management protocol and algorithm that can be used by WISE servers.
4707602
An initial strategy for comparing proteins at the domain architecture level
MOTIVATION
Ideally, only proteins that exhibit highly similar domain architectures should be compared with one another as homologues or be classified into a single family. By combining three different indices, the Jaccard index, the Goodman-Kruskal gamma function and the domain duplicate index, into a single similarity measure, we propose a method for comparing proteins based on their domain architectures.


RESULTS
Evaluation of the method using the eukaryotic orthologous groups of proteins (KOGs) database indicated that it allows the automatic and efficient comparison of multiple-domain proteins, which are usually refractory to classic approaches based on sequence similarity measures. As a case study, the PDZ and LRR_1 domains are used to demonstrate how proteins containing promiscuous domains can be clearly compared using our method. For the convenience of users, a web server was set up where three different query interfaces were implemented to compare different domain architectures or proteins with domain(s), and to identify the relationships among domain architectures within a given KOG from the Clusters of Orthologous Groups of Proteins database.


CONCLUSION
The approach we propose is suitable for estimating the similarity of domain architectures of proteins, especially those of multidomain proteins.


AVAILABILITY
http://cmb.bnu.edu.cn/pdart/.
41885018
A theory of lexicographic multi-criteria optimization
The field of multi-criteria optimization is reviewed as it pertains to lexicographic optimization over real-valued vector spaces. How lexicographic optimization differs from multi-criteria optimization that is restricted to proper Pareto optima is explained. Through a survey of previous work, it is revealed that there are currently no generally applicable methods for solving lexicographic optimization problems, and it is explained that this is due to the lack of an adequate mathematical theory for such problems. A more adequate mathematical theory is then presented for lexicographic optimization in this paper.