57,587 research outputs found
A review on data stream classification
At this present time, the significance of data streams cannot be denied as many researchers have placed their focus on the research areas of databases, statistics, and computer science. In fact, data streams refer to some data points sequences that are found in order with the potential to be non-binding, which is generated from the process of generating information in a manner that is not stationary. As such the typical tasks of searching data have been linked to streams of data that are inclusive of clustering, classification, and repeated mining of pattern. This paper presents several data stream clustering approaches, which are based on density, besides attempting to comprehend the function of the related algorithms; both semi-supervised and active learning, along with reviews of a number of recent studies
Nonlinear projective filtering in a data stream
We introduce a modified algorithm to perform nonlinear filtering of a time
series by locally linear phase space projections. Unlike previous
implementations, the algorithm can be used not only for a posteriori processing
but includes the possibility to perform real time filtering in a data stream.
The data base that represents the phase space structure generated by the data
is updated dynamically. This also allows filtering of non-stationary signals
and dynamic parameter adjustment. We discuss exemplary applications, including
the real time extraction of the fetal electrocardiogram from abdominal
recordings.Comment: 8 page
Extension of Decision Tree Algorithm for Stream Data Mining Using Real Data
Recently, because of increasing amount of data in the society, data stream mining targeting large scale data has
attracted attention. The data mining is a technology of discovery new knowledge and patterns from the massive amounts of data, and what the data correspond to data stream is data stream mining. In this paper, we propose the feature selection with online decision tree. At first, we construct online type decision tree to regard credit card transaction data as data stream on data stream
mining. At second, we select attributes thought to be important for detection of illegal use. We apply VFDT (Very Fast Decision Tree learner) algorithm to online type decision tree construction
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