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KDD
1994
ACM
82views Data Mining» more  KDD 1994»
14 years 29 days ago
Architectural Support for Data Mining
Oneof the mainobstacles in applying data mining techniques to large, real-world databasesis the lack of efficient data management.In this paper, wepresent the design and implement...
Marcel Holsheimer, Martin L. Kersten
KDD
1995
ACM
67views Data Mining» more  KDD 1995»
14 years 13 days ago
A Perspective on Databases and Data Mining
We discuss the use of database met hods for data mining. Recently impressive results have been achieved for some data mining problems using highly specialized and clever data stru...
Marcel Holsheimer, Martin L. Kersten, Heikki Manni...
ICDM
2007
IEEE
122views Data Mining» more  ICDM 2007»
14 years 3 months ago
Zonal Co-location Pattern Discovery with Dynamic Parameters
Zonal co-location patterns represent subsets of featuretypes that are frequently located in a subset of space (i.e., zone). Discovering zonal spatial co-location patterns is an im...
Mete Celik, James M. Kang, Shashi Shekhar
GFKL
2007
Springer
184views Data Mining» more  GFKL 2007»
14 years 23 days ago
FSMTree: An Efficient Algorithm for Mining Frequent Temporal Patterns
Research in the field of knowledge discovery from temporal data recently focused on a new type of data: interval sequences. In contrast to event sequences interval sequences contai...
Steffen Kempe, Jochen Hipp, Rudolf Kruse
SIGKDD
2008
125views more  SIGKDD 2008»
13 years 8 months ago
Incremental pattern discovery on streams, graphs and tensors
Incremental pattern discovery targets streaming applications where the data continuously arrive incrementally. The questions are how to find patterns (main trends) incrementally; ...
Jimeng Sun