Mining Maximal Cliques from an Uncertain Graph

We consider mining dense substructures (maximal cliques) from an uncertain graph, which is a probability distribution on a set of deterministic graphs. For parameter 0 < {\alpha} < 1, we present a precise definition of an {\alpha}-maximal clique in an uncertain graph. We present matching upper and lower bounds on the number of {\alpha}-maximal cliques possible within an uncertain graph. We present an algorithm to enumerate {\alpha}-maximal cliques in an uncertain graph whose worst-case runtime is near-optimal, and an experimental evaluation showing the practical utility of the algorithm.Comment: ICDE 201

FP-Growth Tree Based Algorithms Analysis: CP-Tree and K Map

We propose a novel frequent-pattern tree (FP-tree) structure; our performance study shows that the FP-growth method is efficient and scalable for mining both long and short frequent patterns, and is about an order of magnitude faster than the Apriori algorithm and also faster than some recently reported new frequent-pattern mining methods. FP-tree method is efficient algorithm in association mining to mine frequent patterns in data mining, in spite of long or short frequent data patterns. By using compact best tree structure and partitioning-based and divide-and-conquer data mining searching method, it can be reduces the costs searchsubstantially .it just as the analysis multi-CPU or reduce computer memory to solve problem. But this approach can be apparently decrease the costs for exchanging and combining control information and the algorithm complexity is also greatly decreased, solve this problem efficiently. Even if main adopting multi-CPU technique, raising the requirement is basically hardware, best performanceimprovement is still to be limited. Is there any other way that most one may it can reduce these costs in FP-tree construction, performance best improvement is still limited

Component Outage Estimation based on Support Vector Machine

Predicting power system component outages in response to an imminent hurricane plays a major role in preevent planning and post-event recovery of the power system. An exact prediction of components states, however, is a challenging task and cannot be easily performed. In this paper, a Support Vector Machine (SVM) based method is proposed to help estimate the components states in response to anticipated path and intensity of an imminent hurricane. Components states are categorized into three classes of damaged, operational, and uncertain. The damaged components along with the components in uncertain class are then considered in multiple contingency scenarios of a proposed Event-driven Security-Constrained Unit Commitment (E-SCUC), which considers the simultaneous outage of multiple components under an N-m-u reliability criterion. Experimental results on the IEEE 118-bus test system show the merits and the effectiveness of the proposed SVM classifier and the E-SCUC model in improving power system resilience in response to extreme events

Item-centric mining of frequent patterns from big uncertain data

Item-centric mining of frequent patterns from big uncertain dat