Grid based method & model based clustering method
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The document discusses several grid-based, density-based, and conceptual clustering algorithms. Grid-based approaches like STING and WAVECLUSTER cluster data by quantizing space into grids or cells. CLIQUE uses a grid-based approach to identify dense units of data. Conceptual clustering algorithms like COBWEB create hierarchical cluster trees to classify objects based on attribute probabilities.
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bigdata
bigdatarajshreemuthiah Nadar Saraswati college of arts & sciences offers a M.Sc in information technology with a focus on advanced analytics and big data. This program provides opportunities for students to analyze data in real-time and gain insights across production, supply chain, and other areas to streamline processes, reduce costs, and increase competitiveness. Capgemini helps organizations improve business operations efficiency through insight-driven operations, smart asset management to lower costs and improve availability, and operational analytics solutions to respond faster to changing events with greater insights.
polymorphism
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Grid based method & model based clustering method
- 2. INTRODUCTION STING WAVECLUSTER CLIQUE-Clustering in QUEST FAST PROCESSING TIME
- 3. The grid based clustering approach uses a multi resolution grid data structure. The object space is quantized into finite number of cells that form a grid structure. The major advantage of this method is fast processing time. It is dependent only on the number of cells in each dimension in the quantized space.
- 4. Statistical information GRID. Spatial area is divided into rectangular cells Several levels of cells-at different levels of resolution High level cell is partitioned into several lower level cells. Statistical attributes are stored in cell. (mean , maximum , minimum)
- 5. Computation is query independent Parallel processing-supported. Data is processed in a single pass Quality depends on granuerily
- 7. A multi-resolution clustering approach which applies wavelet transform to the feature space A wavelet transform is a signal processing technique that decomposes a signal into different frequency sub-band Both grid-based and density-based Input parameters: # of cells for each dimension The wavelet , and the # of application wavelet transform.
- 9. Complexity O(N) Detect arbitrary shaped clusters at different scales. Not sensitive to noise , not sensitive to input order. Only applicable to low dimensional data.
- 10. CLIQUE can be considered as both density- based and grid-based 1.It partitions each dimension into the same number of equal length interval. 2.It partitions an m-dimensional data space into non-overlapping rectangular units. 3.A unit is dense if the fraction of total data points contained in the unit exceeds the input model parameter. 4.A cluster is a maximal set of connected dense units within a subspace.
- 11. Attempt to optimize the fit between the data and some mathematical model. ASSUMPTION:-data are generated by a mixture of underlying portability distributes. TECHNIQUES: expectation-maximization Conceptual clustering Neural networks approach
- 12. ITERATIVE REFINEMENT ALGORITHM- used to find parameter estimates EXTENSION OF K-MEANS Assigns an object to a cluster according to a weight representing portability of membership. Initial estimate of parameters Iteratively reassigns scores.
- 13. A form of clustering in machine learning Produces a classification scheme for a set of unlabeled objects. Finds characteristics description for each concept COBWEB A popular and simple method of incremental conceptual learning. Creates a hierarchical clustering in the form of a classification tree.
- 15. Represent each cluster as an exemplar , acting as a “prototype” of the cluster. New objects are distributed to the cluster whose exemplar is the most similar according to some distance measure. SELF ORGANIZING MAP Competitive learning Involves a hierarchical architecture of several units Organization of units-forms a feature map Web document clustering.
- 16. FEATURE TRANSFORMATION METHODS PCA , SVD-Summarize data by creating linear combinations of attributes. But do not remove any attributes ; transformed attributes-complex to interpret FEATURE SELECTION METHODS Most relevant of attributes with represent to class labels Entropy analysis .