The dissemination of data stream systems, wireless networks and mobile devices motivates the need for an efficient data analysis tool capable of gaining insights about these continuous data streams.

Developments in data streams, coupled with the growth in mobile and pervasive devices, have led to the emergence of Ubiquitous Data Mining (UDM). UDM aims to perform data stream mining in a ubiquitous environment with resource-constrained and/or mobile devices. Over the past few years, stream mining techniques have attracted the attention of the data mining community. However these techniques have not addressed the problems imposed by applying the mining technique in a ubiquitous environment. Algorithm Output Granularity (AOG) has been proposed as a generic approach to enable resource-awareness in data stream mining through adaptation. AOG has been applied to lightweight mining techniques and proved its efficiency. Due to the generality of the approach, we propose to apply AOG to an efficient stream clustering technique: Very Fast K-Means (VFKM). It is an extension of K-Means for data stream clustering. VFKM is able to deal with continuous data rather than a static dataset. In this paper, we propose and develop a resource-aware version of Very Fast K-Means to enable its operation for UDM applications. Our model for Resource-Aware Very Fast K-Means (RA-VFKM) is able to adapt to variations in memory availability on mobile devices. We have experimentally demonstrated that such an adaptation enables our RA-VFKM to converge and provide results in situations (such as critically low available memory) where VFKM tends to result in an execution failure.

Abstract- Road crashes cost Australia $15 billion a year and 95 % of these are attributed to drivers ' errors. Risk assessment is at the core of the road safety problem. This paper presents an Advanced Driving Assistance System (ADAS), called SAWUR, that analyses situational driver behaviour and proposes real-time countermeasures to minimise fatalities/casualties. The system is based on Ubiquitous Data Mining (UDM) concepts. It fuses and analyses different types of information from crash data and physiological sensors to diagnose driving risks in realtime. The novelty of our approach consists of augmenting the diagnosis through UDM with associated countermeasures based on a context awareness mechanism. In other words, our system diagnoses and chooses a countermeasure by taking into account the contextual situation of the driver and the road conditions. The types of context we exploit include vehicle dynamics, drivers ’ physiological condition, driver’s profile and environmental conditions. The rationale for exploiting contextual information is to increase the accuracy of the diagnosis (90%) and to reduce false alarm rates (below 1%). The ultimate goal is to decrease driver’s exposure to risks.

declarations are made: I hereby declare that this thesis contains no material which has been accepted for the award of any other degree of diploma at any university or equivalent institution and that, to the best of my knowledge and belief, this thesis contains no material previously published or written by another person, except where due reference is made in the text of the thesis.

Abstract—Website plays a significant role in success of an e-business. It is the main start point of any organization and corporation for its customers, so it's important to customize and design it according to the visitors ' preferences. Also, websites are a place to introduce services of an organization and highlight new service to the visitors and audiences. In this paper, we will use web usage mining techniques, as a new field of research in data mining and knowledge discovery, in an Iranian government website. Using the results, a framework for web content layour is proposed. An agent is designed to dynamically update and improve web links locations and layout. Then, we will explain how it is used to directly enable top managers of the organization to influence on the arrangement of web contents and also to enhance customization of web site navigation due to online users ' behaviors. Keywords—Web usage mining, website design, agent, website customization. I.

Data and Information or Knowledge has a significant role on human activities. Data mining is the knowledge discovery process by analyzing the large volumes of data from various perspectives and summarizing it into useful information. Due to the importance of extracting knowledge/information from the large data repositories, data mining has become an essential component in various fields of human life.