As technology advances, streams of data can be produced in many applications such as social networks, sensor networks, bioinformatics, and chemical informatics. These kinds of streaming data share a property in common—namely, they can be modeled in terms of graph-structured data. Here, the data streams generated by graph data sources in these applications are graph streams. To extract implicit,...

An outlier in a dataset is an observation or a point that is considerably dissimilar to or inconsistent with the remainder of the data. Detection of such outliers is important for many applications and has recently attracted much attention in the data mining research community. In this paper, we present a new method to detect outliers by discovering frequent patterns (or frequent itemsets) from...

Various grammar compression algorithms have been proposed in the last decade. A grammar compression is a restricted CFG deriving the string deterministically. An efficient grammar compression develops a smaller CFG by finding duplicated patterns and removing them. This process is just a frequent pattern discovery by grammatical inference. While we can get any frequent pattern in linear time usi...

Object search in a visual scene is a highly challenging and computationally intensive task. Most of the current object detection techniques extract features from images for classification. From the results of these techniques it can be observed that the feature extraction approach works well for single images but are not sufficient for generalizing over a variety of object instances of the same...

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...

Irrelevant attributes add noise to high dimensional clusters and make traditional clustering techniques inappropriate. Projected clustering algorithms have been proposed to find the clusters in hidden subspaces. We realize the analogy between mining frequent itemsets and discovering the relevant subspace for a given cluster. We propose a methodology for finding projected clusters by mining freq...

Finding frequent patterns from databases has been the most time consuming process of the association rule mining. Till date, a large number of algorithms have been proposed in the area of frequent pattern generation. However, all of these algorithms produce output only at the completion and are not amenable to the real-time need. The need for real-time frequent pattern mining for online tasks a...

We present a framework for characterizing spike (and spiketrain) synchrony in parallel neuronal spike trains that is based on identifying spikes with what we call influence maps: real-valued functions describing an influence region around the corresponding spike times within which possibly graded synchrony with other spikes is defined. We formalize two models of synchrony in this framework: the...

In drahtlosen Sensornetzen ist die effiziente und möglichst sensorlokale Analyse der Daten ein wichtiger Ansatz zur Verlängerung der Batterielaufzeit der Sensoren. Neben den klassischen primitiven Analyseverfahren, wie zum Beispiel Filtern oder Aggregation, werden zunehmend auch komplexe Verfahren teilweise oder vollständig innerhalb der Grenzen der Sensornetze verarbeitet. Die vorliegende Arbe...

Data mining is the collection of techniques for the resourceful, automatic discovery of previously unknown, suitable, novel, helpful and understandable patterns in large databases. Frequent pattern mining has emerged as a vital task in data mining. Frequent patterns are those that occur frequently in a data set. In traditional frequent pattern mining, patterns and items within the patterns are ...