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.
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.
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 .