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Spyware is a kind of malicious code that is installed on victims' machines without their content. They spy on the users' behavior and compromise their privacy, while transmitting sensitive information to some remote servers. Current anti-spyware tools are similar to anti-virus products in that they identify known spyware by comparing the binary image to a database of signatures. Unfortunately, these...
The probabilistic diagnosis model is useful in many fields such as distributed network, digital system level testing, wafer fault testing. Some topologies and continuous defect units distributions are studied in the previous work. In this paper, we extend the model to arbitrary topology structure with share nodes and to the discrete defect distributions, such as Poission distribution and binomial...
This paper introduces a new technique of document clustering based on frequent senses. The proposed system, GDClust (graph-based document clustering) works with frequent senses rather than frequent keywords used in traditional text mining techniques. GDClust presents text documents as hierarchical document-graphs and utilizes an apriori paradigm to find the frequent subgraphs, which reflect frequent...
Recent research in mining user access patterns for predicting Web page requests focuses only on consecutive sequential Web page accesses, i.e., pages which are accessed by following the hyperlinks. In this paper, we propose a new method for mining user access patterns that allows the prediction of multiple non-consecutive Web pages, i.e., any pages within the Web site. Our approach consists of two...
The service discovery based on semantic description plays an important role in the process of Web service composition. Traditional approaches to modeling semantic similarity between Web services compute subsume relationship for function parameters in service profiles within a single ontology. In this paper, we introduce a new graph theoretic framework based on bipartite graph matching for finding...
The efficiency of data mining algorithms is a very important issue as data becoming larger and larger. Density-based clustering analysis can discover clusters with arbitrary shape and is insensitive to noise data. The advantage of grid-based clustering method is linear time complexity. In this paper, we present a new clustering algorithm CLUGD relying on grid and density. We first construct a grid...
In the past years quite a lot of algorithms concerning frequent graph pattern mining have been published. In this paper an overview on the different methods for graph data mining is given, starting with the greedy searches proposed in the middle of the nineties. The ILP-based approaches are taken into account as well as ideas influenced by basket analyses proposed lately. A remaining question is how...
Exploratory analysis of multidimensional data sets is challenging because of the difficulty in comprehending more than three dimensions. Two fundamental statistical principles for the exploratory analysis are (1) to examine each dimension first and then find relationships among dimensions, and (2) to try graphical displays first and then find numerical summaries (D.S. Moore, (1999). We implement these...
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