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Table 3.3: Algorithmic Strategies in Spatial Data Mining

in Chapter 3 Trends in Spatial Data Mining
by Shashi Shekhar, Pusheng Zhang, Yan Huang, Raju Vatsavai

Table 3.4: Interest Measures of Patterns for Classical Data Mining and Spatial Data Mining

in Chapter 3 Trends in Spatial Data Mining
by Shashi Shekhar, Pusheng Zhang, Yan Huang, Raju Vatsavai

Table 1 Mining Data Stream Algorithms

in unknown title
by unknown authors 2004
"... In PAGE 4: ...Table1 . [15] summarizes the most cited data stream mining techniques according to the mining task, the used approach and the status of implementation.... ..."
Cited by 1

Table 1. The computed \g close to quot; relation. The detailed computation process is not presented here since it is similar to mining association rules for exact spatial relationships to be presented below. Since many people may not be satis ed with approximate spatial relation- ships, such as g close to, more detailed spatial computation often needs to be performed to nd the re ned (or precise) spatial relationships in the spatial predicate hierarchy. Thus we have the following steps. Re ned computation is performed on the large predicate sets, i.e., those retained in the g close to table. Each g close to predicate is replaced by one or a set of concrete predicate(s) such as intersect, adjacent to, close to, inside, etc. Such a process results in Table 2. Town Water Road Boundary

in Discovery of Spatial Association Rules in Geographic Information Databases
by Krzysztof Koperski, Jiawei Han 1995
"... In PAGE 9: ...ines: any mines; and (5) boundary: only the boundary of B.C., and U.S.A. Secondly, the \generalized close to quot; (g close to) relationship between (large) towns and the other four classes of entities is computed at a relatively coarse resolution level using a less expensive spatial algorithm such as the MBR data structure and a plane sweeping algorithm [18], or R*-trees and other approxi- mations [5]. The derived spatial predicates are collected in a \g close to quot; table ( Table1 ), which follows an extended relational model: each slot of the table may contain a set of entries. The support of each entry is then computed and those whose support is below the minimum support threshold, such as the column \mine quot;, are removed from the table.... ..."
Cited by 126

Table 12. Implemented data mining and statistical methods

in Comparative Evaluation of Microarray-based Gene Expression Databases
by Hong-hai Do, Toralf Kirsten, Erhard Rahm 2003
"... In PAGE 16: ... Data Mining and Statistics. Table12 shows the data mining and statistical methods currently implemented in the different databases. We can observe that GeneX, SMD and M-CHIPS offer the most comprehensive facilities for data mining, allowing the user to perform various clustering methods, such as the hierarchical and K-means algorithms.... ..."
Cited by 4

Table 4. CPU time for execution of mining algorithm vs. number of containments mined for Oracle data

in Mining Interval Time Series
by Roy Villafane Kien, Kien A. Hua, Duc Tran, Basab Maulik 1999
Cited by 7

Table 4. CPU time for execution of mining algorithm vs. number of containments mined for Oracle data

in Mining Interval Time Series
by Roy Villafane, Kien A. Hua, Duc Tran, Basab Maulik

Table III shows results for 4 data mining based algorithms used

in Comparing Performance of Recommendation Techniques in the Blogsphere
by Kyumars Sheykh Esmaili, Mahmood Neshati, Mohsen Jamali, Hassan Abolhassani, Jafar Habibi

Table III shows results for 4 data mining based algorithms used

in Proceedings of the ECAI 2006 Workshop on Recommender Systems
by Alexander Felfernig, Markus Zanker

Table 1. Overview of the MineBench data mining benchmark suite Application Category Description

in Performance characterization of data mining applications using MineBench
by Joseph Zambreno, Berkin Özıs. Ikyılmaz, Gokhan Memik, Alok Choudhary 2006
"... In PAGE 2: ... In establishing MineBench, we based our se- lection of categories on how commonly these applications are used in industry, and how likely they are to be used in the future. The twelve applications that currently comprise MineBench are listed in Table1 , and are described in more detail in the following sections. Note that these are full- fledged application implementations of these algorithms (as opposed to stand-alone algorithmic modules), which have been extensively optimized to remove all implementation inefficiencies.... ..."
Cited by 3
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