39369999
Maintaining discovered frequent itemsets: Cases for changeable database and support
Mining frequent itemsets from large databases has played an essential role in many data mining tasks. It is also important to maintain the discovered frequent itemsets for these data mining tasks when the database is updated. All algorithms proposed so far for the maintenance of discovered frequent itemsets are only performed with a fixed minimum support, which is the same as that used to obtain the discovered frequent itemsets. That is, users cannot change the minimum support even if the new results are unsatisfactory to the users. In this paper two new complementary algorithms, FMP (First Maintaining Process) and RMP (Repeated Maintaining Process), are proposed to maintain discovered frequent itemsets in the case that new transaction data are added to a transaction database. Both algorithms allow users to change the minimum support for the maintenance processes. FMP is used for the first maintaining process, and when the result derived from the FMP is unsatisfactory, RMP will be performed repeatedly until satisfactory results are obtained. The proposed algorithms re-use the previous results to cut down the cost of maintenance. Extensive experiments have been conducted to assess the performance of the algorithms. The experimental results show that the proposed algorithms are very resultful compared with the previous mining and maintenance algorithms for maintenance of discovered frequent itemsets.
236427
Efficient XML query processing in mediators
With the advent of XML as the standard for data representation and exchange over the Internet, there are ever increasing applications; E-commerce application in particular, using XML as a mediator model. It raises the issue of how to evaluate XML queries efficiently in mediator systems. The authors address the problem of multiple query optimization in an XML based mediator. Because the XML query language differs from SQL/OQL in its pattern matching and regular path expression constructs, the method of identifying common subexpressions of multiple XML queries is different accordingly. We present the steps to identify common subexpressions. To obtain the global query plan, we propose some heuristics to reduce the whole search space. By using a statistical method to estimate the usage frequency of the common subexpressions in XML queries, a selective caching and materializing of remote data can be accurately made, and this will greatly improve query response time.
43500111
An Efficient Text Categorization Algorithm Based on Category Memberships
Text Categorization is the process of automatically assigning predefined categories to free text documents. Although there have existed a large number of text classification algorithms, most of them are either inefficient or too complex. In this paper, we propose the concept of category memberships, which stand for the degrees that words belonging to categories. Based on category memberships, a simple but efficient algorithm is presented. To evaluate our new algorithm, we have conducted experiments using Newsgroup_18828 text collection to compare it with Naive Bayes and k-NN. Experimental results show that our algorithm outperforms Naive Bayes and k-NN if a suitable category membership function is adopted.
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.
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.
36655206
Understanding User Operations on Web Page in WISE
Unlike Internet portals, a typical Web Information System contains lots of user operations. However, existing Web design frameworks focus exclusively on data presentation: the process of user operations is still achieved through low-level programming. That makes WIS hard to be constructed or maintained. In a Web Information System auto-construction Environment (WISE), the translation of user operations to concrete data processing code in persistent data sources is explored. This paper discusses the semantics of typical operations. The implementation code of those operations can be auto-generated with the aid of the mapping from template attributes to persistent objects.
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 .