iii ACKNOWLEDGEMENTS There are so many that have shaped this endeavor in so many ways, it is impossible to thank all of them. I apologize for any omissions, and I hope you understand you are in my mind though not in this section. First and foremost I would like to thank my parents for taking the courage to send their 18 year old child to United States. While I pursued my education at Tech, I kn...

Online monitoring of data streams poses a challenge in many data-centric applications, such as telecommunications networks, traffic management, trend-related analysis, webclick streams, intrusion detection, and sensor networks. Mining techniques employed in these applications have to be efficient in terms of space usage and per-item processing time while providing a high quality of answers to (...

Sequential pattern mining is the mining of data sequences for frequent sequential patterns with time sequence, which has a wide application. Data streams are streams of data that arrive at high speed. Due to the limitation of memory capacity and the need of real-time mining, the results of mining need to be updated in real time. Multiple data streams are the simultaneous arrival of a plurality ...

This paper presents a novel approach, called MODS, to build an accurate time evolving classifier from multiple one-class data streams learning time evolving classifier. Our proposed MODS approach works in two steps. In the first step, we first construct local one-class classifiers on the labeled positive examples from each sub-data stream respectively. We then collect the informative examples (...

Traffic flow might be limited by cross-traffic which has priority. A typical example of such a situation is location where cyclists or pedestrians cross stream car traffic. Splitting the into two separate sub-streams (for instance left–right and right–left) can increase capacity main stream. This because it no longer necessary to have sufficiently large gap in both simultaneously. paper introdu...

Abstract Over the decade, a number of attempts have been made towards data stream clustering, but most works fall under clustering by example approach. There are applications where variable approach is required which involves multiple streams as opposed to examples in stream. Furthermore, few presented for and these applicable numeric only. Hence, this research gap has motivated current work. I...