18072223
Mining association rules with linguistic terms
Some problems of mining association rules with linguistic terms are discussed. First, an incremental updating algorithm of association rules with linguistic terms is presented. The collection of frequent linguistic attribute sets and its negative border along with their support count are maintained, which makes scan the entire database once at most in the process of updating association rules. The experiment shows that the updating algorithm can not only update association rules effectively but also avoid the repeated cost. Secondly, the parallel algorithm for mining association rules with linguistic terms is presented. The Boolean parallel mining algorithm is improved to discover frequent linguistic attribute sets, and the association rules with at least confidence are generated on all processors. This parallel mining algorithm has fine scale-up, size-up and speed-up.
34666787
Applying Agent into Web Testing and Evolution
Automatic Web testing and evolution is an effective method to ensure the quality of Web applications. In this paper, we apply Agent into this area, i.e. firstly, we explain the necessity and feasibility, and also give the general description of our method; then, we apply Agent into the Web performance testing, Web regression testing and Web usability evolution; conclusion remarks are given at last.
30598486
Regression testing for Web applications based on slicing
Web applications have rapid developing speed and changeable user demands, so the regression testing is much important. Since the changed demands result in different versions of Web applications, and the faults usually hiding in the adjusted contents, the regression testing must cover all the related pages. In order to carry through the regression testing quickly and effectively, we make the simplification with the method of slicing. Firstly, we analyze the possible changes in the Web applications and the influences produced by these changes, discussing in the direct-dependent and indirect-dependent way; next, we give the regression testing method based on slicing emphasized on the indirect-dependent among data, i.e., obtaining the dependent set of changed variables by forward and backward search method and generating the testing suits; conclusion remarks and future work are given at last.
8227025
A Browser Compatibility Testing Method Based on Combinatorial Testing
For ensuring the displaying effects of Web applications, it is important to perform compatibility testing for browsers under the different configurations and it is a hard task to test all. So this paper focused on the improvements for browser compatibility testing. Firstly, we introduced the related work. Then we analysed the functional speciality of the current popular browsers, and provided the preconditions to simplify problems. Next we gave an instance and brought forward the single factor covering method and the pair-wise covering design to gain testing suits. Finally, we introduced the assistant tools we have developed and the future work.
31813405
Website evolution based on statistic data
Maintenance and evolution are critical for website since the requirements often change, the developing cycle is short, while the life cycle is long. In order to make progress in this area, we focus on the users' responses and attitudes. So we begin our work at the log files on the side of website server, gathering the users' visiting information and the server's responses. Thus we obtain our weighted structure model. Then, we analyze and discuss such information to identify the key pages, the predominate pages and the users' visiting patterns. And we can improve the testing efficiency based on these knowledge. All these will help to improve the structure of the site, fulfill the functionality of the site, and enhance users' visiting efficiency.
32570723
Parallel algorithm for mining fuzzy association rules
The principle and steps of the algorithm for mining fuzzy association rules is studied, and the parallel algorithm for mining fuzzy association rules is presented. In this parallel mining algorithm, quantitative attributes are partitioned into several fuzzy sets by the parallel fuzzy c-means algorithm, and fuzzy sets are applied to soften the partition boundary of the attributes. Then, the parallel algorithm for mining Boolean association rules is improved to discover frequent fuzzy attributes. Last, the fuzzy association rules with at least fuzzy confidence are generated on all processors. The parallel mining algorithm is implemented on the distributed linked PC/workstation. The experiment results show that the parallel mining algorithm has fine scaleup, sizeup and speedup.
33924916
Geometric Algorithms for Density-Based Data Clustering
We present new geometric approximation and exact algorithms for the density-based data clustering problem in d-dimensional space Rd (for any constant integer d ? 2). Previously known algorithms for this problem are efficient only for uniformly-distributed points. However, these algorithms all run in ?(n2) time in the worst case, where n is the number of input points. Our approximation algorithm based on the ?-fuzzy distance function takes O(n log n) time for any given fixed value ? > 0, and our exact algorithms take sub-quadratic time. The running times and output quality of our algorithms do not depend on any particular data distribution. We believe that our fast approximation algorithm is of considerable practical importance, while our sub-quadratic exact algorithms are more of theoretical interest. We implemented our approximation algorithm and the experimental results show that our approximation algorithm is efficient on arbitrary input point sets.
16379968
On Structure of Some Plane Graphs with Application to Choosability
A graph G=(V, E) is (x, y)-choosable for integers xy?1 if for any given family {A(v)?v?V} of sets A(v) of cardinality x, there exists a collection {B(v)?v?V} of subsets B(v)?A(v) of cardinality y such that B(u)?B(v)=? whenever uv?E(G). In this paper, structures of some plane graphs, including plane graphs with minimum degree 4, are studied. Using these results, we may show that if G is free of k-cycles for some k?{3, 4, 5, 6}, or if any two triangles in G have distance at least 2, then G is (4m, m)-choosable for all nonnegative integers m. When m=1, (4m, m)-choosable is simply 4-choosable. So these conditions are also sufficient for a plane graph to be 4-choosable.
12925025
The 4-Choosability of Plane Graphs without 4-Cycles'
A graphGis calledk-choosable ifkis a number such that if we give lists ofkcolors to each vertex ofGthere is a vertex coloring ofGwhere each vertex receives a color from its own list no matter what the lists are. In this paper, it is shown that each plane graph without 4-cycles is 4-choosable.
6156170
On edge domination numbers of graphs
Let @c"s^'(G) and @c"s"s^'(G) be the signed edge domination number and signed star domination number of G, respectively. We prove that 2n-4>[email protected]"s"s^'(G)>[email protected]"s^'(G)>=n-m holds for all graphs G without isolated vertices, where n=|V(G)|>=4 and m=|E(G)|, and pose some problems and conjectures.
31739418
Geometric Algorithms for Density-based Data Clustering
Data clustering is a fundamental problem arising in many practical applications. In this paper, we present new geometric approximation and exact algorithms for the density-based data clustering problem in d-dimensional space ℝd (for any constant integer d ≥ 2). Previously known algorithms for this problem are efficient only when the specified range around each input point, called the δ-neighborhood, contains on average a constant number of input points. Different distributions of the input data points have significant impact on the efficiency of these algorithms. In the worst case when the data points are highly clustered, these algorithms run in quadratic time, although such situations might not occur very frequently on real data. By using computational geometry techniques, we develop faster approximation and exact algorithms for the density-based data clustering problem in ℝd. In particular, our approximation algorithm based on the ∊-fuzzy distance function takes O(n log n) time for any given fixed value ∊>0, and our exact algorithms take sub-quadratic time. The running times and output quality of our algorithms do not depend on any particular data distribution. We believe that our fast approximation algorithm is of considerable practical importance, while our sub-quadratic exact algorithms are more of theoretical interest. We implemented our approximation algorithm and the experimental results show that our approximation algorithm is efficient on arbitrary input point sets.