By Sankar K Pal; Pabitra Mitra
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Extra resources for Pattern recognition algorithms for data mining : scalability, knowledge discovery and soft granular computing / [...] XA-GB
Let ng denote the © 2004 by Taylor & Francis Group, LLC Multiscale Data Condensation 33 current number of samples in GRABBAG whenever Step 1 of the algorithm is entered. 1. Use the k-NN rule with the current contents of STORE to classify the ith point from GRABBAG. If classiﬁed correctly the point is returned to GRABBAG; otherwise, it is placed in STORE. Repeat this operation for i = 1, 2, . . , ng . 2. If one complete pass is made through Step 1 with no transfer from GRABBAG to STORE, or the GRABBAG is exhausted then terminate; else go to Step 1.
Clustering is used in several exploratory data analysis tasks, customer retention and management, and web mining. The clustering problem has been studied in many ﬁelds, including statistics, machine learning and pattern recognition. However, large data considerations were absent in these approaches. Recently, several new algorithms with greater emphasis on scalability have been developed, including those based on summarized cluster representation called cluster feature (Birch , ScaleKM ), sampling (CURE ) and density joins (DBSCAN ).
Each level of the tree represents a partition of the feature space at a particular scale of detail. Prediction for a query point is performed using blocks from diﬀerent scales; ﬁner scale blocks are used for points close to the query and cruder scale blocks for those far from the query. However, the blocks are constructed by simple median splitting algorithms which do not directly consider the density function underlying the data. We describe in this chapter a density-based multiresolution data reduction algorithm  that uses discs of adaptive radii for both density estimation and sample pruning.
Pattern recognition algorithms for data mining : scalability, knowledge discovery and soft granular computing / [...] XA-GB by Sankar K Pal; Pabitra Mitra