Numeric Attributes

2020 ◽  
pp. 29-60
Keyword(s):  
Numeric Attributes

User Evaluation of Numeric Attributes of Automatically-Generated Reroutes

2011 ◽  
Author(s):  
James DeArmon ◽  
Daniel Greenbaum ◽  
Karol Kerns ◽  
Craig Wanke ◽  
Chau Nguyen
Keyword(s):  
User Evaluation ◽  
Numeric Attributes

Numeric Attributes

2018 ◽  
pp. 33-62
Keyword(s):  
Numeric Attributes

Handling Numeric Attributes in Hoeffding Trees

2008 ◽  
pp. 296-307 ◽  
Author(s):  
Bernhard Pfahringer ◽  
Geoffrey Holmes ◽  
Richard Kirkby
Keyword(s):  
Numeric Attributes

Mining optimized support rules for numeric attributes

2001 ◽  
Vol 26 (6) ◽  
pp. 425-444 ◽  
Author(s):  
Rajeev Rastogi ◽  
Kyuseok Shim
Keyword(s):  
Numeric Attributes

Mining optimized association rules with categorical and numeric attributes

2002 ◽  
Vol 14 (1) ◽  
pp. 29-50 ◽  
Author(s):  
R. Rastogi ◽  
Kyuseok Shim
Keyword(s):  
Association Rules ◽  
Numeric Attributes

scholarly journals A Fast K-prototypes Algorithm Using Partial Distance Computation

2017 ◽  
Author(s):  
Byoungwook KIM
Keyword(s):  
Minimum Distance ◽  
Clustering Algorithm ◽  
Cluster Center ◽  
Maximum Difference ◽  
Distance Computation ◽  
Computational Performance ◽  
Categorical Attributes ◽  
Data Objects ◽  
Numeric Data ◽  
Numeric Attributes

The k-means is one of the most popular and widely used clustering algorithm, however, it is limited to only numeric data. The k-prototypes algorithm is one of the famous algorithms for dealing with both numeric and categorical data. However, there have been no studies to accelerate k-prototypes algorithm. In this paper, we propose a new fast k-prototypes algorithm that gives the same answer as original k-prototypes. The proposed algorithm avoids distance computations using partial distance computation. Our k-prototypes algorithm finds minimum distance without distance computations of all attributes between an object and a cluster center, which allows it to reduce time complexity. A partial distance computation uses a fact that a value of the maximum difference between two categorical attributes is 1 during distance computations. If data objects have m categorical attributes, maximum difference of categorical attributes between an object and a cluster center is m. Our algorithm first computes distance with only numeric attributes. If a difference of the minimum distance and the second smallest with numeric attributes is higher than m, we can find minimum distance between an object and a cluster center without distance computations of categorical attributes. The experimental shows proposed k-prototypes algorithm improves computational performance than original k-prototypes algorithm in our dataset.

scholarly journals An Approach to Mining Two-Dimensional Optimized Association Rules for Numeric Attributes

2007 ◽  
Vol 18 (10) ◽  
pp. 2528 ◽  
Author(s):  
Zhi HE
Keyword(s):  
Association Rules ◽  
Two Dimensional ◽  
Numeric Attributes

Reduct Calculation and Discretization of Numeric Attributes in Entity Attribute Value Model

2018 ◽  
pp. 464-478
Author(s):  
Wojciech Świeboda ◽  
Nguyen Sinh Hoa
Keyword(s):  
Value Model ◽  
Numeric Attributes ◽  
Attribute Value
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