We study overall storage overflow problem in sensor networks, wherein data-collecting base station is not available while more data is generated than available storage spaces in the entire network. Existing research designs a two-stage solution to solve this problem. It first aggregates overflow data to the size that can be accommodated by the available storage capacity in the network, and then offloads the aggregated data into the network to be stored. We refer to this naive two-stage solution as DAO-N. In this paper, we demonstrate that this approach does not necessarily achieve good performance. We propose a more unified method that is based upon data replication techniques, referred to as DAO-R, in order to improve the performance of DAO-N. Specifically, we design two energy-efficient data replication algorithms to integrate data aggregation and data offloading in DAO-N. We show via extensive simulations that DAO-R outperforms DAO-N by around 30% in terms of energy consumption under different network parameters.

Learning association rules and/or associative classification rules has been extensively studied in data mining and knowledge discovery community. Associative classification rules are considered as constrained association rules. Mining traditional association rules from transaction databases, however, has suffered some limitations. One of these limitations is that each transaction merely contains binary items with each item either present or absent in a transaction. Another limitation is that only positive association rules are discovered. Mining fuzzy association rules and discovering negative association rules have been developed to overcome these limitations, respectively. In this paper, we briefly introduce fuzzy association rules and negative association rules and especially discuss and compare what negative association rules look like. Approaches to discovering fuzzy association rules and negative association rules are combined to propose a new interestingness measure for both positive and negative fuzzy association rules. With this interestingness measure, an algorithm for mining those rules is described. Also, the special forms of association rules are presented, called associative classification rules. The algorithm to mining positive and negative fuzzy association rules is extended to learn positive and negative fuzzy associative classification rules.

We present a biomedical literature data mining system SPIE-DM (Scalable and Portable Information Extraction and Data Mining) to extract and mine the protein-protein interaction network from biomedical literature such as MedLine. SPIE-DM consists of two phases: in phase 1, we develop a scalable and portable ie method (SPIE) to extract the protein-protein interaction from the biomedical literature. These extracted protein-protein interactions form a scale-free network graph. In phase 2, we apply a novel clustering method SFCluster to mine the protein-protein interaction network. The clusters in the network graph represent some potential protein complexes, which are very important for biologist to study the protein functionality. The clustering algorithm considers the characteristics of the scale-free network graphs and is based on the local density of the vertex and its neighborhood functions that can be used to find more meaningful clusters at different density levels. The experiments of SPIE-DM on around 1600 chromatin proteins indicate that our system is very promising for extracting and mining from biomedical literature databases.

Current search engines maintain a local repository to improve the search efficiency. A crawler is used to periodically poll the remote web pages to update the contents of the local repository. Due to the resource limitations, some local pages may be stale. To maintain the high freshness of the repository, the crawler is expected to revisit remote web pages in optimized order and frequency. The intuitive metric of freshness of the local repository is defined as the fraction of up-to-date web pages in the repository, which is merely based on the repository content, and does not, unfortunately, reflect the perspective of the search engine users, e.g., how often is a web page queried? We propose a novel weighted metric of the repository freshness with the importance of web pages being the weights. This metric not only takes into account the local web pages themselves but also the perspectives of the search engine users. We study the repository synchronization policy under this new metric, compare this metric with others, analyze its features, and discuss how the web page importance is determined.

In this paper we present a novel method to discover cyber communities from the WWW. A cyber community is a set of highly connected web pages in the WWW, which share the similar topics or interests. The WWW can be modeled as a huge scale-free network graph. Discovering cyber communities is converted to trawling the corresponding web graph to search for approximate p-quasi complete graph. The algorithm presented in this paper considers the characteristics of the scale-free network graphs and is based on the neighborhood information of the vertex in the p-quasi complete graph in the web graph.

We propose an interactive model, RuleViz, for visualizing the entire process of knowledge discovery and data mining.The model consists of ve components according to the main ingredients of the knowledge discovery process: original data visualization, visual data reduction, visual data preprocess, visual rule discovery, and rule visualization.The RuleViz model for visualizing the process of knowledge discovery is introduced and each component is discussed.Two aspects are emphasized, human-machine interaction and process visualization.The interaction helps the KDD system navigate through the enormous search spaces and recognize the intentions of the user, and the visualization of the KDD process helps users gain better insight i n to the multidimensional data, understand the intermediate results, and interpret the discovered patterns.According to the RuleViz model, we implement a n i n teractive system, CViz, which exploits \parallel coordinates" technique to visualize the process of rule induction.The original data is visualized on the parallel coordinates, and can be interactively reduced both horizontally and vertically.Three approaches for discretizing numerical attributes are provided in the visual data preprocessing.CViz learns classi cation rules on the basis of a rule induction algorithm and presents the result as the algorithm proceeds.The discovered rules are nally visualized on the parallel coordinates with each rule being displayed as a directed \polygon", and the rule accuracy and quality are used to render the \polygons" and control the choice of rules to be displayed to avoid clutter.The CViz system has been experimented with the UCI data sets and synthesis data sets, and the results demonstrate that the RuleViz model and the implemented visualization system are useful and helpful for understanding the process of knowledge discovery and interpreting the nal results.