Fuzzy Clustering-Based Quantitative Association Rules Mining in Multidimensional Data Set

2015 ◽  
pp. 68-75 ◽  
Author(s):  
Jining Jia ◽  
Yongzai Lu ◽  
Jian Chu ◽  
Hongye Su
Keyword(s):  
Association Rules ◽  
Fuzzy Clustering ◽  
Multidimensional Data ◽  
Data Set ◽  
Association Rules Mining ◽  
Quantitative Association Rules ◽  
Multidimensional Data Set

Association Rules Mining Based on Adaptive Fuzzy Clustering Algorithm

2014 ◽  
Vol 998-999 ◽  
pp. 842-845 ◽  
Author(s):  
Jia Mei Guo ◽  
Yin Xiang Pei
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Clustering Algorithm ◽  
Original Data ◽  
Data Set ◽  
Association Rules Mining ◽  
Fuzzy Association Rules ◽  
Redundant Data ◽  
Fuzzy Partitions ◽  
Rules Extraction

Association rules extraction is one of the important goals of data mining and analyzing. Aiming at the problem that information lose caused by crisp partition of numerical attribute , in this article, we put forward a fuzzy association rules mining method based on fuzzy logic. First, we use c-means clustering to generate fuzzy partitions and eliminate redundant data, and then map the original data set into fuzzy interval, in the end, we extract the fuzzy association rules on the fuzzy data set as providing the basis for proper decision-making. Results show that this method can effectively improve the efficiency of data mining and the semantic visualization and credibility of association rules.

scholarly journals Clustering algorithms subjected to K-mean and gaussian mixture model on multidimensional data set

2019 ◽  
Vol 7 (2) ◽  
pp. 448 ◽  
Author(s):  
Saadaldeen Rashid Ahmed Ahmed ◽  
Israa Al Barazanchi ◽  
Zahraa A. Jaaz ◽  
Haider Rasheed Abdulshaheed
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Clustering Algorithms ◽  
Gaussian Mixture ◽  
Multidimensional Data ◽  
Data Set ◽  
Multidimensional Data Set

scholarly journals Principal Graphs and Manifolds

2010 ◽  
pp. 28-59 ◽  
Author(s):  
Alexander N. Gorban ◽  
Andrei Y. Zinovyev
Keyword(s):  
Principal Component ◽  
Approximation Problem ◽  
Graph Grammar ◽  
Multidimensional Data ◽  
Data Set ◽  
Expectation Maximisation ◽  
The Family ◽  
Distance Approximation ◽  
Principal Points ◽  
Multidimensional Data Set

In many physical, statistical, biological and other investigations it is desirable to approximate a system of points by objects of lower dimension and/or complexity. For this purpose, Karl Pearson invented principal component analysis in 1901 and found ‘lines and planes of closest fit to system of points’. The famous k-means algorithm solves the approximation problem too, but by finite sets instead of lines and planes. This chapter gives a brief practical introduction into the methods of construction of general principal objects (i.e., objects embedded in the ‘middle’ of the multidimensional data set). As a basis, the unifying framework of mean squared distance approximation of finite datasets is selected. Principal graphs and manifolds are constructed as generalisations of principal components and k-means principal points. For this purpose, the family of expectation/maximisation algorithms with nearest generalisations is presented. Construction of principal graphs with controlled complexity is based on the graph grammar approach.

scholarly journals 3D Visualization of Sound Fields Perceived by an Acoustic Camera

2015 ◽  
Vol 15 (7) ◽  
pp. 45-57
Author(s):  
Nevena Popova ◽  
Georgi Shishkov ◽  
Petia Koprinkova-Hristova ◽  
Kiril Alexiev
Keyword(s):  
3D Visualization ◽  
Multidimensional Data ◽  
Sound Waves ◽  
Signal Processing Algorithm ◽  
Data Set ◽  
3 Dimensional ◽  
Sound Fields ◽  
Neuro Fuzzy ◽  
Real Time Visualization ◽  
Multidimensional Data Set

Abstract The paper summarizes the application results of a recently proposed neuro-fuzzy algorithm for multi-dimensional data clustering to 3-Dimensional (3D) visualization of dynamically perceived sound waves recorded by an acoustic camera. The main focus in the present work is on the developed signal processing algorithm adapted to the specificity of multidimensional data set recorded by the acoustic camera, as well as on the created software package for real-time visualization of the “observed” sound waves propagation.

Uncovering Actionable Knowledge in Corporate Data with Qualified Association Rules

2011 ◽  
Vol 2 (2) ◽  
pp. 1-21 ◽  
Author(s):  
Nenad Jukic ◽  
Svetlozar Nestorov ◽  
Miguel Velasco ◽  
Jami Eddington
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Mining Method ◽  
Data Set ◽  
Association Rules Mining ◽  
Standard Data ◽  
Additional Information ◽  
Day Of The Week ◽  
Mining Methods ◽  
Corporate Actions

Association rules mining is one of the most successfully applied data mining methods in today’s business settings (e.g. Amazon or Netflix recommendations to customers). Qualified association rules mining is an extension of the association rules data mining method, that uncovers previously unknown correlations that only manifest themselves under certain circumstances (e.g. on a particular day of the week), with the goal of improving action results, e.g. turning an underperforming campaign (spread too thin over the entire audience) into a highly targeted campaign that delivers results. Such correlations have not been easily reachable using standard data mining tools so far. This paper describes the method for straightforward discovery of qualified association rules and demonstrates the use of qualified association rules mining on an actual corporate data set. The data set is a subset of a corporate data warehouse for Sam’s Club, a division of Wal-Mart Stores, INC. The experiments described in this paper illustrate how qualified association rules supplement standard association rules data mining methods and provide additional information which can be used to better target corporate actions.

Uncovering Actionable Knowledge in Corporate Data with Qualified Association Rules

2012 ◽  
pp. 210-229
Author(s):  
Nenad Jukic ◽  
Svetlozar Nestorov ◽  
Miguel Velasco ◽  
Jami Eddington
Keyword(s):  
Data Mining ◽  
Data Warehouse ◽  
Association Rules ◽  
Data Set ◽  
Association Rules Mining ◽  
Standard Data ◽  
Additional Information ◽  
Day Of The Week ◽  
Mining Methods ◽  
Corporate Actions

Association rules mining is one of the most successfully applied data mining methods in today’s business settings (e.g. Amazon or Netflix recommendations to customers). Qualified association rules mining is an extension of the association rules data mining method, that uncovers previously unknown correlations that only manifest themselves under certain circumstances (e.g. on a particular day of the week), with the goal of improving action results, e.g. turning an underperforming campaign (spread too thin over the entire audience) into a highly targeted campaign that delivers results. Such correlations have not been easily reachable using standard data mining tools so far. This paper describes the method for straightforward discovery of qualified association rules and demonstrates the use of qualified association rules mining on an actual corporate data set. The data set is a subset of a corporate data warehouse for Sam’s Club, a division of Wal-Mart Stores, INC. The experiments described in this paper illustrate how qualified association rules supplement standard association rules data mining methods and provide additional information which can be used to better target corporate actions.

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