Date stream mining techniques can be used in tracking user behaviors as they attempt to achieve their goals. Quality metrics over stream-mined models identify potential changes in user goal attainment. When the quality of some data mined models varies significantly from nearby models—as defined by quality metrics—then the user’s behavior is automatically flagged as a potentially significant beh...

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 ...

Advanced metering infrastructure (AMI) is an imperative component of the smart grid, as it is responsible for collecting, measuring, analyzing energy usage data, and transmitting these data to the data concentrator and then to a central system in the utility side. Therefore, the security of AMI is one of the most demanding issues in the smart grid implementation. In this paper, we propose an in...

Data stream mining has become a novel research topic of growing interest in knowledge discovery. Most proposed algorithms for data stream mining assume that each data block is basically a random sample from a stationary distribution, but many databases available violate this assumption. That is, the class of an instance may change over time, known as concept drift. In this paper, we propose a S...

Mining patterns that compress the data well was shown to be an effective approach for extracting meaningful patterns and solving the redundancy issue in frequent pattern mining. Most of the existing works in the literature consider mining compressing patterns from a static database of itemsets or sequences. These approaches require multiple passes through the data and do not scale up with the s...

Data stream classification poses many challenges to the data mining community. Here this paper solves all the challenges such as infinite length, concept-drift, concept-evolution, and feature-evolution. Since a data stream is theoretically infinite in length, it is impractical to store and use all the historical data for training. Concept-drift is a common phenomenon in data streams, which occu...

Data stream mining is a process of extracting the information from continuously coming rapid data records. Data stream can be viewed as an ordered sequence of instances appears at time varying. Data stream classification has three major problems: infinite length, concept drift and concept evolution or arrival of novel class. In this paper, we propose a new approach for detection of novel class ...

Cluster analysis has played a key role in data understanding. When such an important data mining task is extended to the context of data streams, it becomes more challenging since the data arrive at a mining system in one-pass manner. The problem is even more difficult when the clustering task is considered in a sliding window model which requiring the elimination of outdated data must be dealt...

The problem of “scaling up for high dimensional data and high speed data streams” is among the “ten challenging problems in data mining research”[36]. This paper is devoted to estimating entropy of data streams. Mining data streams[19, 4, 1, 29] in (e.g.,) 100 TB scale databases has become an important area of research, e.g., [10, 1], as network data can easily reach that scale[36]. Search engi...

The data stream model for data mining places harsh restrictions on a learning algorithm. A model must be induced following the briefest interrogation of the data, must use only available memory and must update itself over time within these constraints. Additionally, the model must be able to be used for data mining at any point in time. This paper describes a data stream classification algorith...