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Table 3.4: Interest Measures of Patterns for Classical Data Mining and Spatial Data Mining
Table 1: Spatial Database topics
2007
"... In PAGE 4: ... Spatial databases are discussed in the context of object-relational databases [42, 141, 143], which provide extensibility to many components of traditional databases to support the spatial domain. Three major areas that receive attention in the database context - conceptual, logical and physical data models - are discussed(see Table1 ). In addition, applications of spatial data for spatial data mining are also explored.... ..."
Table 8 (b): Temporal classification of spatial data classes from the taxonomy
"... In PAGE 4: ...Table8 (a): Temporal classification of spatial data classes from the taxonomy Class I RE S C Sing. TG Ocean Y L S Ice Mass L Y TC Sea Y V ST Lake V Y TC River V Y TC Desert Water V Y TC Island Y S Shore Y Y TC Port Y TS Dam/Weir Y TS Sand Y Y TC Silt Y TC Clay Y TC Rocks Y S Land V Y TS Forest Y L S Farm V Y TS Park V Y TS Bushes V Y TC Summit Y S Mountain Y L S Hill Y V S Valley Y V S Mine V Y TS Grouping spatial objects by temporal attributes produces the classification shown in Table 9.... ..."
Table 6: Trans-collection themes extracted from KDD Abstract Data
2005
"... In PAGE 8: ... As in the news data, we also analyzed the life cycles of trans-collection themes in KDD Abstracts. Seven dominat- ing trans-collection themes are shown in Table6 and the interesting patterns of life cycles are presented in Figure 10 (W = 1). Some new topics, such as spatial-temporal data mining, have not shown up as trans-collection themes, because when we consider the whole stream, they are not among the dominating topics.... ..."
Cited by 24
Table: 1 A classification of data mining and text data mining applications.
1999
Cited by 107
Table 1: A classification of data mining and text data mining applications.
1999
Cited by 107
Table 1 Data mining contributions
2002
Cited by 1
Table 1: Evolution of data mining
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
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
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