Stream analysis is considered as a crucial component of strategic control over a broad variety of disciplines in business, science and engineering. Stream data is a sequence of observations collected over intervals of time. Each data stream describes a phenomenon. Analysis on Stream data includes discovering trends (or patterns) in a Stream sequence. In the last few years, data mining has emerg...

Data stream is continuous flow of data, which necessitates load shedding for data stream processing system. Here we study overload handling for frequent pattern mining indata streams. Here in this paper load shedding use frequent pattern matching algorithm i.e priority, transaction and attribute in overload situation. The heavy workload or continues stream of the mining algorithm lies mostly in...

A data stream is an emerging research area and also a challenging problem in present days. Streaming is a technique for transferring data from one place to another. A data stream is a continuous, real time, uninterrupted sequence of coherent data. The paper presents the overall study about data stream and its process model and structure used for data set preparation in data mining analysis.

In this demo, we show that intelligent load shedding is essential in achieving optimum results in mining data streams under various resource constraints. The Loadstar system introduces load shedding techniques to classifying multiple data streams of large volume and high speed. Loadstar uses a novel metric known as the quality of decision (QoD) to measure the level of uncertainty in classificat...

Frequent itemset mining attempts to find frequent subsets in a transaction database. In this era of big data, demand for frequent itemset mining is increasing. Therefore, the combination of fast implementation and low memory consumption, especially for stream data, is needed. In response to this, we optimize an online algorithm, called Skip LC-SS algorithm [1], for hardware. In this paper, we p...

Data mining represents an exciting and vibrant area of research. In particular, on-line mining has gained significant momentum in recent years. The changing data characteristics and real-time response constraints of streaming data preclude the use of existing mining algorithms that were designed for stored datasets. Therefore, researchers are proposing new fast and light algorithms for on-line ...

In recent years, advances in both hardware and software technologies coupled with high-speed data generation has led to data streams and data stream mining. Data generation has been much faster in data stream applications and scores of data is generated in quick turnaround time. Hence it becomes obvious to perform mining, data on arrival that is usually termed as data stream mining. General fre...

Data stream mining is the process of applying data mining methods to a data stream in real-time in order to create descriptive or predictive models. Due to the dynamic nature of data streams, new classes may emerge as a data stream evolves, and the concept being modeled may change with time. This gives rise to the need to continuously make revisions to the predictive model. Revising the predict...

Mining Web click streams is an important data mining problem with broad applications. However, it is also a difficult problem since the streaming data possess some interesting characteristics, such as unknown or unbounded length, possibly a very fast arrival rate, inability to backtrack over previously arrived click-sequences, and a lack of system control over the order in which the data arrive...

Stream Data Mining is a new emerging topic in the field of research. Today, there are number of application that generate Massive amount of stream data. Examples of such kind of systems are Sensor networks, Real time surveillance systems, telecommunication systems. Hence there is requirement of intelligent processing of such type of data that would help in proper analysis and use of this data i...