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Spatial Co-location Patterns Mining
"... Data mining refers to a process of analyzing data from different perspectives and summarizing it into useful information that can be used in variety of data centric applications in real time. Geographical Information System (GIS) combined with Data Mining has long being an area of research. GIS is a ..."
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discovery is finding coexistence of non-spatial features in a spatial neighborhood. In this paper we have mined co-location patterns with an approach based on participation index and participation ratio. This technique finds the maximal participation index and uses a clustering algorithm. We have used
On the Relationships between Clustering and Spatial Co-location Pattern Mining
- In Proceedings of the 18th IEEE international Conference on Tools with Artificial intelligence (November 13 - 15, 2006). ICTAI. IEEE Computer Society
"... The goal of spatial co-location pattern mining is to find subsets of spatial features frequently located together in spatial proximity. Example co-location patterns include services requested frequently and located together from mobile devices (e.g., PDAs and cellular phones) and symbiotic species i ..."
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Cited by 8 (0 self)
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The goal of spatial co-location pattern mining is to find subsets of spatial features frequently located together in spatial proximity. Example co-location patterns include services requested frequently and located together from mobile devices (e.g., PDAs and cellular phones) and symbiotic species
Statistically Significant Dependencies for Spatial Co-location Pattern Mining and Classification Association Rule Discovery
, 2014
"... Spatial co-location pattern mining and classification association rule discovery are two canonical tasks studied in the data mining community. Both of them focus on the detection of sets of features that show associations. The difference is that in spa-tial co-location pattern mining, the features a ..."
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Spatial co-location pattern mining and classification association rule discovery are two canonical tasks studied in the data mining community. Both of them focus on the detection of sets of features that show associations. The difference is that in spa-tial co-location pattern mining, the features
Mining Of Spatial Co-location Pattern from Spatial
"... Spatial data mining, or knowledge discovery in spatial database, refers to the extraction of implicit knowledge, spatial relations, or other patterns not explicitly stored in spatial databases. Spatial data mining is the process of discovering interesting characteristics and patterns that may implic ..."
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neighborhood. The Previous methods of mining co-location patterns, converts neighborhoods of feature instances to item sets and applies mining techniques for transactional data to discover the patterns, combines the discovery of spatial neighborhoods with the mining process. It is an extension of a spatial
Spatial Interestingness Measures for Co-location Pattern Mining
"... Abstract—Co-location pattern mining aims at finding sub-sets of spatial features frequently located together in spatial proximity. The underlying motivation is to model the spatial correlation structure between the features. This allows to discover interesting co-location rules (feature interactions ..."
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Cited by 1 (0 self)
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Abstract—Co-location pattern mining aims at finding sub-sets of spatial features frequently located together in spatial proximity. The underlying motivation is to model the spatial correlation structure between the features. This allows to discover interesting co-location rules (feature
Survey of clustering data mining techniques
, 2002
"... Accrue Software, Inc. Clustering is a division of data into groups of similar objects. Representing the data by fewer clusters necessarily loses certain fine details, but achieves simplification. It models data by its clusters. Data modeling puts clustering in a historical perspective rooted in math ..."
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Cited by 408 (0 self)
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applications such as scientific data exploration, information retrieval and text mining, spatial database applications, Web analysis, CRM, marketing, medical diagnostics, computational biology, and many others. Clustering is the subject of active research in several fields such as statistics, pattern
Mining Co-locations Under Uncertainty
"... Abstract. A co-location pattern represents a subset of spatial features whose events tend to locate together in spatial proximity. The certain case of the co-location pattern has been investigated. However, location information of spatial features is often imprecise, aggregated, or error prone. Beca ..."
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Abstract. A co-location pattern represents a subset of spatial features whose events tend to locate together in spatial proximity. The certain case of the co-location pattern has been investigated. However, location information of spatial features is often imprecise, aggregated, or error prone
Mining co-location patterns with rare events from spatial data sets
- Geoinformatica
"... Abstract A co-location pattern is a group of spatial features/events that are frequently co-located in the same region. For example, human cases of West Nile Virus often occur in regions with poor mosquito control and the presence of birds. For colocation pattern mining, previous studies often empha ..."
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Cited by 24 (2 self)
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Abstract A co-location pattern is a group of spatial features/events that are frequently co-located in the same region. For example, human cases of West Nile Virus often occur in regions with poor mosquito control and the presence of birds. For colocation pattern mining, previous studies often
Discovering Spatial Co-location Patterns: A Summary of Results
- Lecture Notes in Computer Science
, 2001
"... Given a collection of boolean spatial features, the co-location pattern discovery process finds the subsets of features frequently located together. For example, the analysis of an ecology dataset may reveal the frequent co-location of a fire ignition source feature with a needle vegetation type fea ..."
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Cited by 74 (8 self)
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patterns are proposed which are robust in the face of potentially infinite overlapping neighborhoods. We also propose an algorithm to mine frequent spatial co-location patterns and analyze its correctness, and completeness. We plan to carry out experimental evaluations and performance tuning in the near
Enumeration of Maximal Clique for Mining Spatial Co-location Patterns
, 2007
"... Abstract. In this paper we present a systematic approach to mine co-location patterns in Sloan Digital Sky Survey (SDSS) Data. SDSS Data Release 5 contains 3.6 TB of data. Availability of such large amount of useful data is an obvious opportunity for application of data mining techniques to generate ..."
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Cited by 4 (2 self)
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Abstract. In this paper we present a systematic approach to mine co-location patterns in Sloan Digital Sky Survey (SDSS) Data. SDSS Data Release 5 contains 3.6 TB of data. Availability of such large amount of useful data is an obvious opportunity for application of data mining techniques
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