Frequent itemset mining over dynamic data is an important problem in the context of data mining. The two main factors of data stream mining algorithm are memory usage and runtime, since they are limited resources. Mining frequent pattern in data streams, like traditional database and many other types of databases, has been studied popularly in data mining research. Many applications like stock ...

High utility pattern (HUP) mining over data streams has become a challenging research issue in data mining. The existing sliding window-based HUP mining algorithms over stream data suffer from the level-wise candidate generationand-test problem. Therefore, they need a large amount of execution time and memory. Moreover, their data structures are not suitable for interactive mining. To solve the...

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...

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...

Mining data streams often requires real-time extraction of interesting patterns from dynamic and continuously growing data. This requirement has imposed challenges on discovering and outputting current useful patterns in an instant way, commonly referred to as online streaming data mining. In this paper, we present INSTANT, a novel algorithm that explores maximal frequent itemsequences from str...

In our previous paper, we have proposed a framework for automatically translating tree-processing programs into stream-processing programs. However, in writing programs that require buffering of input data, a user has to explicitly use buffering primitives which copy data from input stream to memory or copy constructed trees from memory to an output stream. Such explicit insertion of buffering ...

In recent years, the mining research over data stream has been prominent as they can be applied in many alternative areas in the real worlds. In [20], a framework for mining frequent itemsets over a data stream is proposed by the use of weighted slide window model. Two algorithms of single pass (WSW) and the WSW-Imp (improving one) using weighted sliding model were proposed in there to solve th...

Machine learning approach has got major importance when distribution of data is unknown. Classification of data from the data set causes some problem when distribution of data is unknown. Characterization of raw data relates to whether the data can take on only discrete values or whether the data is continuous. In real world application data drawn from non-stationary distribution, causes the pr...

In recent years, data mining plays a major role in maintaining the huge volume of data from which it can derive the useful information. With the huge number of formation of data, the data wants to be lectured in a limit to the charge of growth. But it is complex to get over the set of meaningful information from the continuous set of data. Data-stream mining is a method which can discover impor...