The problem considered is that of finding frequent subpaths of a database of paths in a fixed undirected graph. This problem arises in applications such as predicting congestion in network traffic. An algorithm based on Apriori, called AFS, is developed, but with significantly improved efficiency through exploiting the underlying graph structure, which makes AFS feasible for practical input pat...

Mining various types of association rules from supermarket datasets is an important data mining problem. One similar problem involves finding frequent itemsets and then deriving rules from frequent itemsets. The supermarket data is temporal. Considering time attributes in the supermarket dataset some association rules can be extracted which may hold for a small time interval and not throughout ...

We explore the possibility of applying the framework of frequent pattern mining to a class of continuous objects appearing in nature, namely knots. We introduce the frequent knot mining problem and present a solution. The key observation is that a database consisting of knots can be transformed into a transactional database. This observation is based on the Prime Decomposition Theorem of knots.

Mining frequent patterns has been studied popularly in data mining research. All of previous studies assume that items in a pattern are unordered. However, the order existing between items must be considered in some applications. In this paper, we first give the formal model of ordered patterns and discuss the problem of mining frequent ordered patterns. Base on our analyses, we present two eff...

Graph kernels have become a well-established approach in graph mining. One of the early graph kernels, the frequent subgraph kernel, is based on embedding the graphs into a feature space spanned by the set of all frequent connected subgraphs in the input graph database. A drawback of this graph kernel is that the preprocessing step of generating all frequent connected subgraphs is computational...

Recently, an algorithm for merging counter-based data summaries which are the output of the Frequent algorithm (Frequent summaries) has been proposed by Agarwal et al. In this paper, we present a new algorithm for merging Frequent summaries. Our algorithm is fast and simple to implement, and retains the same computational complexity of the algorithm presented by Agarwal et al. while providing b...

A 67-year-old white man presented to Hermann Hospital upon referral from an Austin, Tex, physician for evaluation of recurrent chest pain. Eighteen months before admission the patient first noted episodes of chest discomfort, which he described as a left parasternal sensation of heaviness or tightness variably associated with moderate physical exertion. The discomfort would last 3 to 4 minutes ...

Frequent pattern mining has been a focused theme in data mining research and an important first step in the analysis of data arising in a broad range of applications. The traditional exact model for frequent pattern requires that every item occurs in each supporting transaction. However, real application data is usually subject to random noise or measurement error, which poses new challenges fo...