Classification is a supervised learning technique. Classification arises frequently from bioinformatics such as disease classifications using high throughput data like microarrays. Classification rule mining classifies data in constructing a model based on the training set and the values or class labels in a classifying attribute and uses it in classifying new data. Currently, a various modelin...

Online, one-pass mining Web click streams poses some interesting computational issues, such as unbounded length of streaming data, possibly very fast arrival rate, and just one scan over previously arrived Web click-sequences. In this paper, we propose a new, single-pass algorithm, called DSM-TKP (Data Stream Mining for Top-K Path traversal patterns), for mining a set of top-k path traversal pa...

Much of the research regarding streaming data has focused only on real time querying and analysis of recent data stream allowable in memory. However, as data stream mining, or tracking of past data streams, is often required, it becomes necessary to store large volumes of streaming data in stable storage. Moreover, as stable storage has restricted capacity, past data stream must be summarized. ...

Activity recognition has become one of the emerging applications in the area of ubiquitous computing. This research aims at leveraging ubiquitous data stream mining and context reasoning for mobile activity recognition. The novel system allows dynamic adaptation and personalisation of the learning model to reflect the realistic activity changes emerged over time. Sensors fusion to attain a cont...

Title of dissertation: DESIGN TOOLS FOR DYNAMIC, DATA-DRIVEN, STREAM MINING SYSTEMS Kishan Palintha Sudusinghe, Doctor of Philosophy, 2015 Dissertation directed by: Professor Shuvra S. Bhattacharyya Department of Electrical and Computer Engineering and Institute for Advanced Computer Studies The proliferation of sensing devices and costand energy-efficient embedded processors has contributed to...

The problem of streaming data has gained importance in recent years because of advances in hardware technology. The ubiquitous presence of data streams in a number of practical domains has generated a lot of research in this area. Example applications include surveillance for terrorist attack, network monitoring for intrusion detection, and others. Problems such as data mining which have been w...

High utility sequential pattern mining has emerged as an important topic in data mining. Although several preliminary works have been conducted on this topic, the existing studies mainly focus on mining high utility sequential patterns (HUSPs) in static databases and do not consider the streaming data. Mining HUSPs over data streams is very desirable for many applications. However, addressing t...

The increasing importance of data stream arising in a wide range of advanced applications has led to the extensive study of mining frequent patterns. Mining data streams poses many new challenges amongst which are the one-scan nature, the unbounded memory requirement and the high arrival rate of data streams. In this paper, we propose a new approach for mining itemsets on data stream. Our appro...

The increasing importance of data stream arising in a wide range of advanced applications has led to the extensive study of mining frequent patterns. Mining data streams poses many new challenges amongst which are the one-scan nature, the unbounded memory requirement and the high arrival rate of data streams. In this paper, we propose a new approach for mining itemsets on data stream. Our appro...