Maximal frequent itemsets are one of several condensed representations of frequent itemsets, which store most of the information contained in frequent itemsets using less space, thus being more suitable for stream mining. This paper considers a simple but effective algorithm for mining maximal frequent itemsets over a stream landmark. We design a compact data structure named FP-FOREST to improv...

We investigate how mining top-k largest tiles in a data stream under the sliding window model can be useful for (real-time) analysis of videos and, in particular, for tracking. We first explain how a tracking problem can be cast into a stream pattern mining problem. We then show some preliminary results on tracking in the particular context where both the objects and the camera are moving and w...

Classification of data streams has become an important area of data mining, as the number of applications facing these challenges increases. In this paper, we propose a new ensemble learning method for data stream classification in presence of concept drift. Our method is capable of detecting changes and adapting to new concepts which appears in the stream. Data stream classification; concept d...

Data is the primary concern in data mining. Data Stream Mining is gaining a lot of practical significance with the huge online data generated from Sensors, Internet Relay Chats, Twitter, Facebook, Online Bank or ATM Transactions. The primary constraint in finding the frequent patterns in data streams is to perform only one time scan of the data with limited memory and requires less processing t...

The field of Distributed Data Mining (DDM) deals with the problem of analyzing data by paying careful attention to the distributed computing, storage, communication, and human-factor related resources. Unlike the traditional centralized systems, DDM offers a fundamentally distributed solution to analyze data without necessarily demanding collection of the data to a single central site. This cha...

In USA, 2002, approximately 3.2 million intersection-related crashes occurred, corresponding to 50 percent of all reported crashes. In Japan, more than 58 percent of all traffic crashes occur at intersections. With the advances in Intelligent Transportation Systems, such as off-the-shelf and in-vehicle sensor technology, wireless communication and ubiquitous computing research, safety of inters...

Micro-blogs are a challenging new source of information for data mining techniques. Twitter is a micro-blogging service built to discover what is happening at any moment in time, anywhere in the world. Twitter messages are short, and generated constantly, and well suited for knowledge discovery using data stream mining. We briefly discuss the challenges that Twitter data streams pose, focusing ...

Automated trading systems for financial markets can use data mining techniques for future price movement prediction. However, classifier accuracy is only one important component in such a system: the other is a decision procedure utilizing the prediction in order to be long, short or out of the market. In this paper, we investigate the use of technical indicators as a means of deciding when to ...

Considering the continuity of a data stream, the accessed windows information of a data stream may not be useful as a concept change is effected on further data. In order to support frequent item mining over data stream, the interesting recent concept change of a data stream needs to be identified flexibly. Based on this, an algorithm can be able to identify the range of the further window. A m...

It has been popularly recognized that stream data represents an important form of data, with broad applications. There have been a lot of studies on effective stream data management and query processing, as well as some recent studies on stream data mining. Although this is a promising direction, most existing studies have not paid enough attention to one critical fact: most data streams reside...