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253
A Framework for Multi-objective Clustering and its Application to Co-location Mining
"... Abstract. The goal of multi-objective clustering (MOC) is to decompose a dataset into similar groups maximizing multiple objectives in parallel. In this paper, we provide a methodology, architecture and algorithms that, based on a large set of objectives, derive interesting clusters regarding two or ..."
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provides search engine type capabilities to users, enabling them to query a large set of clusters with respect to different objectives and thresholds. We evaluate the proposed MOC framework in a case study that centers on spatial co-location mining; the goal is to identify regions in which high levels
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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algebraic analysis and extensive experiments that the feature tree based algorithm outperforms uncertain Apriori algorithm by an order of magnitude not only for co-locations of large sizes but also for datasets with high level of uncertainty. This is an important insight in mining uncertainty co-locations
Finding Regional Co-location Patterns for Sets of Continuous Variables, under review
"... This paper proposes a novel framework for mining regional colocation patterns with respect to sets of continuous variables in spatial datasets. The goal is to identify regions in which multiple continuous variables with values from the wings of their statistical distribution are co-located. A co-loc ..."
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Cited by 14 (9 self)
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This paper proposes a novel framework for mining regional colocation patterns with respect to sets of continuous variables in spatial datasets. The goal is to identify regions in which multiple continuous variables with values from the wings of their statistical distribution are co-located. A co-location
Discovering Co-Location Patterns in Datasets with Extended Spatial Objects
"... Abstract. Co-location mining is one of the tasks of spatial data mining, which focuses on the detection of the sets of spatial features frequently located in close proximity of each other. Previous work is based on transaction-free apriori-like algorithms. The approach we propose is based on a grid ..."
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Abstract. Co-location mining is one of the tasks of spatial data mining, which focuses on the detection of the sets of spatial features frequently located in close proximity of each other. Previous work is based on transaction-free apriori-like algorithms. The approach we propose is based on a grid
A Partial Join Approach for Mining Co-location Patterns
- In the Proceeding of ACM International Symposium on Advances in Geographic Information Systems(ACM-GIS
, 2004
"... Spatial co-location patterns represent the subsets of events whose instances are frequently located together in geographic space. We identified the computational bottleneck in the execution time of a current co-location mining algorithm. A large fraction of the join-based co-location miner algorithm ..."
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Cited by 23 (7 self)
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Spatial co-location patterns represent the subsets of events whose instances are frequently located together in geographic space. We identified the computational bottleneck in the execution time of a current co-location mining algorithm. A large fraction of the join-based co-location miner
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
Spatial Data Mining
, 2003
"... Spatial data mining is the process of discovering interesting and previously unknown, but potentially useful, patterns from large spatial datasets. Extracting interesting and useful patterns from spatial datasets is more di#cult than extracting the corresponding patterns from traditional numeric and ..."
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Cited by 35 (8 self)
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and categorical data due to the complexity of spatial data types, spatial relationships, and spatial autocorrelation. This chapter will discuss some of accomplishments and research needs of spatial data mining in the following categories: location prediction, spatial outlier detection, co-location mining
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
A join-less approach for co-location pattern mining: A summary of results
- In IEEE International Conference on Data Mining
, 2005
"... Spatial co-location patterns represent the subsets of features whose instances are frequently located together in geographic space. Co-location pattern discovery presents challenges since the instances of spatial features are embedded in a continuous space and share a variety of spatial relationship ..."
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Cited by 11 (4 self)
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relationships. A large fraction of the computation time is devoted to identifying the instances of co-location patterns. We propose a novel join-less approach for co-location pattern mining, which materializes spatial neighbor relationships with no loss of co-location instances and reduces the computational
Results 1 - 10
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253
