Article history: Received 28 October 2011 Received in revised form 23 June 2013 Accepted 25 June 2013 Available online 13 July 2013 Frequent episode discovery is a popular framework for pattern discovery from sequential data. It has found many applications in domains like alarmmanagement in telecommunication networks, fault analysis in the manufacturing plants, predicting user behavior in web c...

Pattern discovery is at the heart of bioinformatics, and algorithms from computer science have been widely used for identifying biological patterns. The assumption behind pattern discovery approaches is that a pattern that occurs often enough in biological sequences/structures or is conserved across organisms is expected to play a role in defining the respective sequence’s or structure’s functi...

Traditional research in information retrieval (IR) focuses on retrieving documents. This paper introduces the idea that valuable information exists within a document collection as thematic patterns that can be found without retrieving any individual documents from the collection. This pattern information is valuable in its own right and as an aid to the IR search process, and is often not expli...

Web usage mining: automatic discovery of patterns in clickstreams and associated data collected or generated as a result of user interactions with one or more Web sites. This paper describes web usage mining for our college log files to analyze the behavioral patterns and profiles of users interacting with a Web site. The discovered patterns are represented as clusters that are frequently acces...

We are carrying out research into developing several approaches to pattern discovery in protein topology diagrams, and comparing them. The underlying motivation is to eeciently automatically generate patterns classifying sets of proteins and to apply this to characterising databases of protein structure. We are using TOPS protein topology diagrams, which we have formalised as a restricted kind ...

One of the most important data mining problems is discovery of sequential patterns. Sequential pattern mining consists in discovering all frequently occurring subsequences in a collection of data sequences. This paper discusses several issues concerning possible extensions to traditional database management systems required to support sequential pattern discovery: a sequential pattern query lan...

We introduce FuncICA, a new independent component analysis method for pattern discovery in inherently functional data, such as time series data. We show how applying the dual of temporal ICA to temporal data, and likewise applying the dual of spatiotemporal ICA to spatiotemporal data, enables independent component regularization not afforded by the primal forms applied to their original domains...

Application of data mining techniques to the World Wide Web, referred to as Web mining, has been the focus of several recent research projects and papers. However, there is n o established vocabulary, leading to confusion when comparing research efforts. The t e r m W e b mining has been used in two distinct ways. T h e first, called Web content mining in this paper, is the process of informati...

To early detect and defend the threats in the Internet caused by botnet, darknet monitoring is very important to understand various botnet activities. However, common illegal accesses by ordinary malwares makes such detection difficult. In this paper, in order to remove such accesses by ordinary malwares from the results of network monitoring, we propose a data screening method based on finding...

This paper describes an application of the optimized pattern discovery framework to text and Web mining. In particular, we introduce a class of simple combinatorial patterns over phrases, called proximity phrase association patterns, and consider the problem of nding the patterns that optimizes a given statistical measure in a large collection of unstructured texts. For this class of patterns, ...