Time-series stream is one of the most common data types in data mining field. It is prevalent in fields such as stock market, ecology, and medical care. Segmentation is a key step to accelerate the processing speed of time-series stream mining. Previous algorithms for segmenting mainly focused on the issue of ameliorating precision instead of paying much attention to the efficiency. Moreover, t...

Big data quickly comes under the spotlight in recent years. As big data is supposed to handle extremely huge amount of data, it is quite natural that the demand for the computational environment to accelerates, and scales out big data applications increases. The important thing is, however, the behavior of big data applications is not clearly defined yet. Among big data applications, this paper...

A parameter called TDS (Total Dermatoscopic Score), calculated by the wellknown ABCD formula, is frequently used in melanoma diagnosis. In our previous research we found a new formula, similar to the original ABCD formula, that yielded fewer diagnostic errors. This new ABCD formula was developed using data mining techniques, in particular, the rule induction algorithm LEM2, a part of the data m...

A growing number of applications require support for processing data that is in the form of continuous stream, rather than finite stored data. In this paper we present a new approach for detecting temporal patterns with complex predicates over continuous data stream. Our algorithm efficiently scans the stream with a sliding window, and checks the data inside the window from right-to-left to see...

MCE Hassan SJBIT Bangalore Summary Mining Frequent itemsets from transactional data streams is a very challenging task as it has to handle continuous, unbounded, and ordered sequence of data elements generated at a rapid rate in a data stream. In order to enhance the analysis of stream data it is essential to extract frequent itemsets from more recent data. For this purpose a sliding window mec...

In this study, a clustering algorithm that uses DCT transformed data is presented. The algorithm is a grid density-based clustering algorithm that can identify clusters of arbitrary shape. Streaming data are transformed and reconstructed as needed for clustering. Experimental results show that DCT is able to approximate a data distribution efficiently using only a small number of coefficients a...

The process of Ubiquitous data mining (UDM) allows data stream mining operations to be conducted on handheld devices with limited resources. Algorithms which take advantage of visualisation can assist users in understanding and interpreting data mining results more quickly. However, there are currently no on-line real-time visualisation tools to complement the UDM algorithms. In this paper we i...

In recent years, emerging applications introduced new constraints for data mining methods. These constraints are typical of a new kind of data: the data streams. In a data stream processing, memory usage is restricted, new elements are generated continuously and have to be considered as fast as possible, no blocking operator can be performed and the data can be examined only once. At this time ...

We consider the problem of computing information theoretic functions such as entropy on a data stream, using sublinear space. Our first result deals with a measure we call the “entropy norm” of an input stream: it is closely related to entropy but is structurally similar to the well-studied notion of frequency moments. We give a polylogarithmic space one-pass algorithm for estimating this norm ...