The process of Ubiquitous data mining (UDM) allows data stream mining operations to be conducted on handheld devices with limited resources. Algorithms which take advantage of visualisation can assist users in understanding and interpreting data mining results more quickly. However, there are currently no on-line real-time visualisation tools to complement the UDM algorithms. In this paper we i...

In recent years, emerging applications introduced new constraints for data mining methods. These constraints are typical of a new kind of data: the data streams. In a data stream processing, memory usage is restricted, new elements are generated continuously and have to be considered as fast as possible, no blocking operator can be performed and the data can be examined only once. At this time ...

The sensor networks, web click stream and astronomical applications generate a continuous flow of data streams. Most likely data streams are generated in a wireless environment. These data streams challenge our ability to store and process them in real-time with limited computing capabilities of the wireless environment. Querying and mining data streams have attracted attention in the past two ...

Huge volume of data from domain specific applications such as medical, financial, library, telephone, shopping records and individual are regularly generated. Sharing of these data is proved to be beneficial for data mining application. On one hand such data is an important asset to business decision making by analyzing it. On the other hand data privacy concerns may prevent data owners from sh...

Due to recent advances in data collection techniques, massive amounts of data are being collected at an extremely fast pace. Also, these data are potentially unbounded. Boundless streams of data collected from sensors, equipments, and other data sources are referred to as data streams. Various data mining tasks can be performed on data streams in search of interesting patterns. This paper studi...

As a representative sequential pattern mining problem, counting the frequency of serial episodes from a streaming sequence has drawn continuous attention in academia due to its wide application in practice, e.g., telecommunication alarms, stock market, transaction logs, bioinformatics, etc. Although a number of serial episodes mining algorithms have been developed recently, most of them are nei...

Mainly existing sequential pattern mining algorithms are hard to find out long significant time-interval sequential patterns in information stream. In this paper, we propose a new bitmap-based algorithm of mining d time-interval sequential pattern in information stream called DSBMMS, which is based on binary bit counting and multiple time-interval sequential position. We transform the whole seq...

In ubiquitous data stream mining, di®erent devices often aim to learn concepts that are similar to some extent. In many applications, such as spam ̄ltering or news recommendation, the data stream underlying concept (e.g., interesting mail/news) is likely to change over time. Therefore, the resultant model must be continuously adapted to such changes. This paper presents a novel Collaborative Dat...

Due to recent scientific and technological advances in information systems it is now possible to continuously record data at high speeds in a wide range of devices. The need to make sense of such massive amounts of data opens an opportunity to create new data stream classification techniques to model and predict the behavior of streaming data. When learning from data streams, the problem of con...

We propose new algorithms for adaptively mining closed rooted trees, both labeled and unlabeled, from data streams that change over time. Closed patterns are powerful representatives of frequent patterns, since they eliminate redundant information. Our approach is based on an advantageous representation of trees and a low-complexity notion of relaxed closed trees, as well as ideas from Galois L...