This research aims to describe a new design of data stream mining system that can analyze medical data stream and make real-time prediction. The motivation of the research is due to a growing concern of combining software technology and medical functions for the development of software application that can be used in medical field of chronic disease prognosis and diagnosis, children healthcare,...

Acceleration of huge data analysis, especially an analysis of huge, and fast streaming data is one of the major issues in recent computer science. Proper modeling, and understanding of streaming data analysis are indispensable for speed-up, scale out, and faster response time of streaming data analysis. Especially for the research on scheduling, or load balancing algorithms, a model of the targ...

A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. The continuous characteristic of streaming data necessitates the use of algorithms that require only one scan over the stream for knowledge discovery. Data mining over data streams should support the flexible trade-off between processing time and mining accuracy. In many application areas, min...

Mining data streams is an emerging area of research given the potentially large number of business and scientific applications. A significant challenge in analyzing/mining data streams is the high data rate of the stream. In this paper, we propose a novel approach to cope with the high data rate of incoming data streams. We termed our approach “algorithm output granularity”. It is a resource-aw...

Data mining is the information technology that extracts valuable knowledge from large amounts of data. Due to the emergence of data streams as a new type of data, data stream mining has recently become a very important and popular research issue. Privacy preservation issue of data streams mining is very important issue, in this dissertation work, an approach based on Geometric data perturbation...

Closed frequent itemset mining plays an essential role in data stream mining. It could be used in business decisions, basket analysis, etc. Most methods for mining closed frequent itemsets store the streamlined information in compact data structure when data is generated. Whenever a query is submitted, it outputs all closed frequent itemsets. However, the online processing of existing approache...

The traditional preference mining setting, referred to here as the batch setting, has been widely studied in the literature in recent years. However, the dynamic nature of the problem of mining preferences increasingly requires solutions that quickly adapt to change. The main reason for this is that frequently user’s preferences are not static and can evolve over time. In this paper, we formall...

The traditional preference mining setting, referred to here as the batch setting, has been widely studied in the literature in recent years. However, the dynamic nature of the problem of mining preferences increasingly requires solutions that quickly adapt to change. The main reason for this is that frequently user's preferences are not static and can evolve over time. In this article, we addre...

Recently, frequent itemsets mining over data streams attracted much attention. However, mining closed itemsets from data stream has not been well addressed. The main difficulty lies in its high complexity of maintenance aroused by the exact model definition of closed itemsets and the dynamic changing of data streams. In data stream scenario, it is sufficient to mining only approximated frequent...

The frequent items problem is to process a stream of items and find all items occurring more than a given fraction of the time. It is one of the most heavily studied problems in data stream mining, dating back to the 1980s. Many applications rely directly or indirectly on finding the frequent items, and implementations are in use in large scale industrial systems. However, there has not been mu...