By Guandong Xu
Info mining has witnessed colossal advances in fresh a long time. New examine questions and sensible demanding situations have arisen from rising parts and functions in the a number of fields heavily relating to human everyday life, e.g. social media and social networking. This ebook goals to bridge the distance among conventional info mining and the most recent advances in newly rising info companies. It explores the extension of well-studied algorithms and methods into those new examine arenas. Read more...
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This last point is the basis of NMF because we can consider each original document in our example as being built from a small set of hidden features. NMF generates these features. It is useful to think of each feature (column vector) in the features matrix W as a document archetype comprising a set of words where each word’s cell value defines the word’s rank in the feature: The higher a word’s cell value the higher the word’s rank in the feature. A column in the coefficients matrix H represents an original document with a cell value defining the document’s rank for a feature.
Data preparation improves the quality of data and consequently helps improve the quality of data mining results. The well known saying “garbage-in garbage-out” is very relevant to this domain. This chapter contributes to the related topics with respect to data preparation, covering attribute selection, data cleaning and integrity, multiple model integration and so on. 1 Feature Selection Feature selection, also known as variable selection, attribute reduction, feature selection or variable subset selection, is the technique of selecting a subset of relevant features for building robust learning models.
Association rules present the fact that how frequently items are bought together. For example, an association rule “beer-diaper (70%)” indicates that 70% of the customers who bought beer also bought diapers. Such rules can be used to make predictions and recommendations for customers and design then store layout. , sequence, tree, graph) has been thoroughly explored for the past decade. , itemset, sequence, tree, and graph. 1 Chapter 7 This chapter reports the latest research progress in clustering analysis from three different aspects: (1) improve the clustering result quality of heuristic clustering algorithm by using Space Smoothing Search methods; (2) use approximate backbone to capture the common optimal information of a given data set, and then use the approximate backbone to improve the clustering result quality of heuristic clustering algorithm; (3) design a local significant unit (LSU) structure to capture the data distribution in high-dimensional space to improve the clustering result quality based on kernel estimation and spatial statistical theory.
Applied Data Mining by Guandong Xu