Concepts inAdaptive non-linear clustering in data streams
Data stream
In telecommunications and computing, a data stream is a sequence of digitally encoded coherent signals used to transmit or receive information that is in the process of being transmitted. In electronics and computer architecture, a data flow determines for which time which data item is scheduled to enter or leave which port of a systolic array, a Reconfigurable Data Path Array or similar pipe network, or other processing unit or block.
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Cluster analysis
Cluster analysis or clustering is the task of assigning a set of objects into groups (called clusters) so that the objects in the same cluster are more similar (in some sense or another) to each other than to those in other clusters. Clustering is a main task of explorative data mining, and a common technique for statistical data analysis used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics.
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Streaming algorithm
In computer science, streaming algorithms are algorithms for processing data streams in which the input is presented as a sequence of items and can be examined in only a few passes (typically just one). These algorithms have limited memory available to them (much less than the input size) and also limited processing time per item. These constraints may mean that an algorithm produces an approximate answer based on a summary or "sketch" of the data stream in memory.
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Novelty detection
Novelty detection is the identification of new or unknown data or signals that a machine learning system is not aware of during training. Novelty detection is one-class classification. The known data form one class, and a novelty-detection method tries to identify outliers that differ from the distribution of ordinary data, which formed the single data class. Compared to multi-class classification, one-class classification is useful if outliers are sparse compared to ordinary data.
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Linear separability
In geometry, two sets of points in a two-dimensional space are linearly separable if they can be completely separated by a single line. In general, two point sets are linearly separable in n-dimensional space if they can be separated by a hyperplane. In more mathematical terms: Let and be two sets of points in an n-dimensional space. Then and are linearly separable if there exists n+1 real numbers, such that every point satisfies and every point satisfies, where is the i:th component of
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Locality of reference
In computer science, locality of reference, also known as the principle of locality, is the phenomenon of the same value or related storage locations being frequently accessed. There are two basic types of reference locality. Temporal locality refers to the reuse of specific data and/or resources within relatively small time durations. Spatial locality refers to the use of data elements within relatively close storage locations.
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