| A hyperclique pattern is a new type of association pattern that contains items which are highly affiliated with each other. Specifically, the presence of an item in one transaction strongly implies the presence of every other item that belongs to the same hyperclique pattern. In this dissertation, we present an algorithm for mining maximal hyperclique patterns, which specifies a more compact representation of hyperclique patterns and are desirable for many applications, such as pattern-based clustering. Our algorithm exploits key advantages of both the Depth First Search (DFS) strategy and the Breadth First Search (BFS) strategy. Indeed, we adapt the equivalence pruning method, one of the most efficient pruning methods of the DFS strategy, into the process of the BFS strategy. Our experimental results show that the performance of our algorithm can be orders of magnitude faster than standard maximal frequent pattern mining algorithms, particularly at low levels of support.;With the development of biology, microarray technology has been used to analyze relations between genes, cells and the other biological samples. Obviously, the microarray profiles are data sets containing quantitative attributes, where the relation values are not binary values. If we partition quantitative attributes into Boolean attributes there is potential information loss due to partitioning. Here, we design a new normalization scheme for such kind of dataset with quantitative attributes. Additionally, we adapt the algorithm structures of three popular association pattern mining algorithms and add a critical clique pruning technique for the hyperclique patterns in the normalized datasets.;Mobile ad hoc networks are the next generation in communication networks. Devices can enter or leave the network anytime, devices can be mobile, and no centralized control points or base stations are necessary. This flexibility makes mobile ad hoc networks very interesting for the consumer and military market, but also makes them more complex than many existing wireless networks (such as GSM). Minimum-Weight Connected Dominating Set (MWCDS) problem is to select a vertex subset with minimum weight for a given unit disk graph, such that each vertex of the graph is contained in this subset or has a neighbor in this subset. Besides, the subgraph induced by this vertex subset is connected. It will be beneficial if we can get minimum weighted connected dominating set in the wireless network. We presents a (10 + &egr;)-approximation algorithm to compute MWCDS in unit disk graphs. |
