7609568
Mining Maximal Correlated Member Clusters in High Dimensional Database
Mining high dimensional data is an urgent problem of great practical importance. Although some data mining models such as frequent patterns and clusters have been proven to be very successful for analyzing very large data sets, they have some limitations. Frequent patterns are inadequate to describe the quantitative correlations among nominal members. Traditional cluster models ignore distances of some pairs of members, so a pair of members in one big cluster may be far away. As a combination and complementary of both techniques, we propose the Maximal-Correlated-Member-Cluster (MCMC) model in this paper. The MCMC model is based on a statistical measure reflecting the relationship of nominal variables, and every pair of members in one cluster satisfy unified constraints. Moreover, in order to improve algorithm's efficiency, we introduce pruning techniques to reduce the search space. In the first phase, a Tri-correlation inequation is used to eliminate unrelated member pairs, and in the second phase, an Inverse-Order-Enumeration-Tree (IOET) method is designed to share common computations. Experiments over both synthetic datasets and real life datasets are performed to examine our algorithm's performance. The results show that our algorithm has much higher efficiency than the naive algorithm, and this model can discover meaningful correlated patterns in high dimensional database.
15025287
An Ontology Based Approach to Construct Behaviors in Web Information Systems
System behaviors specify the major functions of domain specific Web Information Systems (WIS). Traditional techniques can not satisfy various requirements or manage innumerous data while developing WIS behaviors. People appeal to a smart tool for implementing the WIS behaviors. This article makes the following contributions: (1) Proposes the concept of domain ontology and behavior ontology to describe the contents and operations of WIS; (2) Extends traditional ECA model to characterize the triggers, parameters, actions as well as validations of WIS behaviors; (3) Analyses the relationships between domain ontology and behavior ontology with rule sets; (4) Implements a tool named WISE Builder with four algorithms to help users building behavior ontology with domain ontology; (5)Shows the feasibility of this technique in a real application case.
35000642
WIEAS: Helping to Discover Web Information Sources and Extract Data from Them
In recent years, more and more information appeared on the web. Extracting information from the web and converting them into regular format become significantly important work. After observing a number of web sites, we found that most of useful information is contained in the web sources, which have a large number of similarly structured web documents. So in this paper we present an approach for discovering the useful information sources from the web and extracting information from them. A useful web information source discovering method and a novel information extraction method are proposed. We also develop a prototype system WIEAS (Web Information Extraction, Analysis And Services) to implement our idea, and use the information extracted by WIEAS to provide plentiful services.
12258714
An Efficient Approach for Interactive Mining of Frequent Itemsets
There have been many studies on efficient discovery of frequent itemsets in large databases. However, it is nontrivial to mine frequent itemsets under interactive circumstances where users often change minimum support threshold (minsup) because the change of minsup may invalidate existing frequent itemsets or introduce new frequent itemsets. In this paper, we propose an efficient interactive mining technique based on a novel vertical itemset tree (VI-tree) structure. An important feature of our algorithm is that it does not have to re-examine the existing frequent itemsets when minsup becomes small. Such feature makes it very efficient for interactive mining. The algorithm we proposed has been implemented and its performance is compared with re-running Eclat, a vertical mining algorithm, under different minsup. Experimental results show that our algorithm is over two orders of magnitude faster than the latter in average.
28786670
A Non-VSM kNN Algorithm for Text Classification
The text classification problem, which is the task of assigning natural language texts to predefined categories based on their content, has been widely studied. Traditional text classification use VSM (Vector Space Model), which views documents as vectors in high dimensional spaces, to represent documents. In this paper, we propose a non-VSM kNN algorithm for text classification. Based on correlations between categories and features, the algorithms first get k F-C tuples, which are the first k tuples in term of correlation value, from an unlabeled document. Then the algorithm predicts the category of the unlabeled documents via these tuples. We have evaluated the algorithm on two document collections and compared it against traditional kNN. Experimental results show that our algorithm outperforms traditional kNN in both efficiency and effectivity.
38613630
Mining Association Rules from Distorted Data for Privacy Preservation
In order to improve privacy preservation and accuracy, we present a new association rule mining scheme based on data distortion. It consists of two steps: First, the original data are distorted by a new randomization method. Then, the mining algorithm is implemented to find frequent itemsets from the distorted data, and generate association rules. With reasonable selection for the random parameters, our scheme can simultaneously provide a higher privacy preserving level to the users and retain a higher accuracy in the mining results.
2552931
KBERG: KnowledgeBase for Estrogen Responsive Genes
Estrogen has a profound impact on human physiology affecting transcription of numerous genes. To decipher functional characteristics of estrogen responsive genes, we developed KnowledgeBase for Estrogen Responsive Genes (KBERG). Genes in KBERG were derived from Estrogen Responsive Gene Database (ERGDB) and were analyzed from multiple aspects. We explored the possible transcription regulation mechanism by capturing highly conserved promoter motifs across orthologous genes, using promoter regions that cover the range of [−1200, +500] relative to the transcription start sites. The motif detection is based on ab initio discovery of common cis-elements from the orthologous gene cluster from human, mouse and rat, thus reflecting a degree of promoter sequence preservation during evolution. The identified motifs are linked to transcription factor binding sites based on the TRANSFAC database. In addition, KBERG uses two established ontology systems, GO and eVOC, to associate genes with their function. Users may assess gene functionality through the description terms in GO. Alternatively, they can gain gene co-expression information through evidence from human EST libraries via eVOC. KBERG is a user-friendly system that provides links to other relevant resources such as ERGDB, UniGene, Entrez Gene, HomoloGene, GO, eVOC and GenBank, and thus offers a platform for functional exploration and potential annotation of genes responsive to estrogen. KBERG database can be accessed at .