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A Joinless Approach for Mining Spatial Colocation Patterns

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/3-540-48482-5_15openalex-snapshot
Multi-way Spatial Joins Using R-Trees: Methodology and Performance Evaluation
inferredcites →https://doi.org/10.1007/3-540-60159-7_4openalex-snapshot
Discovery of spatial association rules in geographic information databases
inferredcites →https://doi.org/10.1016/b0-12-369398-5/00337-6openalex-snapshot
Spatial Databases
inferredcites →https://doi.org/10.1109/icdm.2001.989534openalex-snapshot
Frequent subgraph discovery
inferredcites →https://doi.org/10.1109/icdm.2002.1184038openalex-snapshot
gSpan: graph-based substructure pattern mining
inferredcites →https://doi.org/10.1109/icdm.2005.8openalex-snapshot
A Join-Less Approach for Co-Location Pattern Mining: A Summary of Results
inferredcites →https://doi.org/10.1145/1032222.1032258openalex-snapshot
A partial join approach for mining co-location patterns
inferredcites →https://doi.org/10.1145/303976.303981openalex-snapshot
Processing and optimization of multiway spatial joins using R-trees
inferredcites →https://doi.org/10.1145/502512.502564openalex-snapshot
Mining frequent neighboring class sets in spatial databases
inferredcites →https://doi.org/10.1145/846170.846173openalex-snapshot
A bibliography of temporal, spatial and spatio-temporal data mining research

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Delaunay triangulation‐based spatial colocation pattern mining without distance thresholds
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A Delaunay diagram‐based Min–Max CP‐Tree algorithm for Spatial Data Analysis
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A Parallel Joinless Algorithm for Co-location Pattern Mining Based on Group-Dependent Shard
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Mining Colocation from Big Geo-Spatial Event Data on GPU
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Discovering Spatial Co-location Patterns with Dominant Influencing Features in Anomalous Regions
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Bounding Box Representation of Co-location Instances for $$L_{\infty }$$ Induced Distance Measure
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Interactive Mining of User-Preferred Co-location Patterns Based on SVM
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Mining Maximal Sub-prevalent Co-location Patterns Based on k-hop
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Mining the Potential Relationships Between Cancer Cases and Industrial Pollution Based on High-Influence Ordered-Pair Patterns
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Mining $$\epsilon $$-Closed High Utility Co-location Patterns from Spatial Data
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Spatial Data Science
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Discovering Prevalent Weighted Co-Location Patterns on Spatial Data Without Candidates
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Fast Mining Prevalent Co-location Patterns Over Dense Spatial Datasets
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Continuous Sub-prevalent Co-location Pattern Mining
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Co-location Pattern Mining Under the Spatial Structure Constraint
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Efficient Mining of High Utility Co-location Patterns Based on a Query Strategy
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Efficiently Discovering Spatial Prevalent Co-location Patterns Without Distance Thresholds
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MPCD: An Algorithm for Discovering Multilevel Prevalent Co-location Patterns from Heterogeneous Distribution of Spatial Datasets
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Discovering Spatial Prevalent Co-location Patterns by Once Scanning Datasets Without Generating Candidates
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Efficiently Mining High Average Utility Co-location Patterns Using Maximal Cliques and Pruning Strategies
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An Efficient Clique-Based Level-Wise Search Algorithm for Spatial Prevalent Co-location Pattern Mining
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Density and Connectivity-Based Neighbor Relationship Materialization for Discovering Prevalent Co-location Patterns
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Colocation Mining: Identifying Regional Patterns with a Memory-Efficient Approach
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Mitigating Weight Explosion in Rare-Feature Co-location Pattern Mining: A Cauchy Distribution-Based Approach
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Discovering Statistically Significant Co-location Rules in Datasets with Extended Spatial Objects
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A Parallel Algorithm for Building iCPI-trees
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Statistically Significant Co-location Pattern Mining
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RCP Mining: Towards the Summarization of Spatial Co-location Patterns
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Statistically Significant Co-location Pattern Mining
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Effectively Updating High Utility Co-location Patterns in Evolving Spatial Databases
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Mining Co-locations from Continuously Distributed Uncertain Spatial Data
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Co-location Detector: A System to Find Interesting Spatial Co-locating Relationships
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Efficiently Mining High Utility Co-location Patterns from Spatial Data Sets with Instance-Specific Utilities
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Mining Competitive Pairs Hidden in Co-location Patterns from Dynamic Spatial Databases
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Co-location Rules Discovery Process Focused on Reference Spatial Features Using Decision Tree Learning
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Spatial and Spatiotemporal Big Data Science
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Grid-Based Colocation Mining Algorithms on GPU for Big Spatial Event Data: A Summary of Results
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Mining Co-location Patterns with Dominant Features
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Maximal Sub-prevalent Co-location Patterns and Efficient Mining Algorithms
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A Parallel Spatial Co-location Pattern Mining Approach Based on Ordered Clique Growth
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Spatial Co-location Pattern Mining Based on Density Peaks Clustering and Fuzzy Theory
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Pre-processing Large Spatial Data Sets with Bayesian Methods
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Composite Spatio-Temporal Co-occurrence Pattern Mining
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Mining Spread Patterns of Spatio-temporal Co-occurrences over Zones
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Finding N-Most Prevalent Colocated Event Sets
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Mining Spatial Co-location Patterns with Dynamic Neighborhood Constraint
inferred← citeshttps://doi.org/10.1007/978-3-642-20841-6_29openalex-snapshot
Mining Maximal Co-located Event Sets
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Maximal Cliques Generating Algorithm for Spatial Co-location Pattern Mining
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A New Data Mining Approach to Find Co-location Pattern from Spatial Data
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SSCP: Mining Statistically Significant Co-location Patterns
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Discovery of Regional Co-location Patterns with k-Nearest Neighbor Graph
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Extracting Prevalent Co-location Patterns from Historic Spatial Data
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Mining Co-locations from Spatially Uncertain Data with Probability Intervals
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Discovering Co-location Patterns in Datasets with Extended Spatial Objects
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On Co-occurrence Pattern Discovery from Spatio-temporal Event Stream
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Mining High Utility Co-location Patterns Based on Importance of Spatial Region
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Evaluation Metrics of Spatial and Spatiotemporal Data Mining Techniques
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Introduction
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Interactively Post-mining User-Preferred Co-location Patterns with a Probabilistic Model
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Vector-Degree: A General Similarity Measure for Co-location Patterns
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Maximal Prevalent Co-location Patterns
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Maximal Sub-prevalent Co-location Patterns
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SPI-Closed Prevalent Co-location Patterns
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Top-k Probabilistically Prevalent Co-location Patterns
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Non-redundant Prevalent Co-location Patterns
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Dominant Spatial Co-location Patterns
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High Utility Co-location Patterns
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High Utility Co-location Patterns with Instance Utility
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On Discovering High Utility Core Co-location Patterns From Spatial Datasets
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Fuzzy Spatial High Utility Co-location Patterns Mining Based on Extended Regions
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Effectively Discovering Co-location Patterns Including Rare Features from Spatial Data Using Log-Adaptive Rare Feature Weighting
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UCPM-CPI: A United Co-Location Pattern Mining Algorithm Based on Correlated Participation Index
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Efficient High Utility Co-Location Pattern Mining Under Positive and Negative Utility Constraints
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Sustained Evolving Co-occurrence Pattern Mining
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Mining Prevalent Co-location Patterns with Multiple Minimum Prevalence Thresholds
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Efficient Redundancy Elimination to Discovering Concise Prevalent Co-location Patterns
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A High Utility Co-location Pattern Mining Algorithm Using Multiple Utility Thresholds
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A New Method of Mining High Utility Co-location Patterns from Spatial Data
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Discovering High Average Utility Co-location Patterns Using an Upper Bound Utility and a Hierarchical Instance Tree
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An Algorithm for Discovering Prevalent Co-location Patterns with Considering Both Density and Connectivity
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Extracting Representative Co-location Patterns Considering Distributions of Spatial Features and Instances
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Mining Maximal High Utility Co-location Patterns from Large-Scale Spatial Data
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RCPM_RLM: A Regional Co-location Pattern Mining Method Based on Representation Learning Model
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Local Co-location Pattern Mining Based on Regional Embedding
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SCPM-R+ER: A R+ER-based Algorithm for Mining Spatial Co-location Patterns
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Efficient Multilevel Spatial Co-location Pattern Discovery Based on Density-Wise Clustering and MC-Hash Structure
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MPCPM: Multi-level Prevalent Co-location Pattern Miner
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Discovering Prevalent Co-location Patterns Without Collecting Co-location Instances
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Discovering Maximal High Utility Co-location Patterns from Spatial Data
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Spatial Co-location Pattern Mining—A Survey of Recent Trends
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A Co-occurrence Prediction Framework in Location-Based Social Networks
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Efficient pruning strategies for mining high utility co-location patterns with negative utility features
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A MapReduce approach for spatial co-location pattern mining via ordered-clique-growth
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Domain-driven co-location mining
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Mining regional co-location patterns with kNNG
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Mining maximal sub-prevalent co-location patterns
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Mining high influence co-location patterns from instances with attributes
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Mining Interpretable Regional Co-location Patterns Based on Urban Functional Region Division
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Mining Sustained Emerging Spatio-Temporal Co-occurrence Patterns from Dynamic Spatial Databases
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Spatial and Spatiotemporal Data Mining
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Efficiently Mining Colocation Patterns for Range Query
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Understanding the spatial organization of urban functions based on co-location patterns mining: A comparative analysis for 25 Chinese cities
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Discovering the joint influence of urban facilities on crime occurrence using spatial co-location pattern mining
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Significant spatial co-distribution pattern discovery
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Efficient spatial co-location pattern mining on multiple GPUs
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MCHT: A maximal clique and hash table-based maximal prevalent co-location pattern mining algorithm
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Spatial co-location pattern mining over extended objects based on cell-relation operations
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Meta-PCP: A concise representation of prevalent co-location patterns discovered from spatial data
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An approach based on maximal cliques and multi-density clustering for regional co-location pattern mining
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Efficiently mining spatial co-location patterns utilizing fuzzy grid cliques
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A fast spatial high utility co-location pattern mining approach based on branch-and-depth-extension
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Revealing association rules within intricate ecosystems: A spatial co-location mining method based on Geo-Eco knowledge graph
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Mining strong symbiotic patterns hidden in spatial prevalent co-location patterns
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Representative co-location pattern post-mining based on maximal row instances representation model
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Mining co-location evolving sequences from spatio-temporal data
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Parallel Approach for Finding Co-location Pattern – A Map Reduce Framework
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Discovering Co-location Patterns from Spatial Domain using a Delaunay Approach
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Co-location decision tree for enhancing decision-making of pavement maintenance and rehabilitation
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Exploratory analysis of the interrelations between co-located boolean spatial features using network graphs
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Spatial co-location pattern mining of facility points-of-interest improved by network neighborhood and distance decay effects
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Multi-level method for discovery of regional co-location patterns
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A spatial co-location mining algorithm that includes adaptive proximity improvements and distant instance references
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A visualization approach for discovering colocation patterns
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A statistical method for detecting spatiotemporal co-occurrence patterns
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Mining spatiotemporal association patterns from complex geographic phenomena
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An adaptive detection of multilevel co-location patterns based on natural neighborhoods
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Discovering co-location patterns in multivariate spatial flow data
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Discovery of statistically significant regional co-location patterns on urban road networks
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Mining regional co-location patterns in urban areas with considerations of temporal accessibility and regional structure
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Mining regional patterns of land use with adaptive adjacent criteria
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A Method of Mining Spatial High Utility Co-location Patterns Based on Feature Actual Participation Weight
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The Internet of Things: A Review of Enabled Technologies and Future Challenges
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TRICE: Mining Frequent Itemsets by Iterative TRimmed Transaction LattICE in Sparse Big Data
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D-GENE: Deferring the GENEration of Power Sets for Discovering Frequent Itemsets in Sparse Big Data
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Finding associations-between-groups in multimode networks
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A novel method on incremental mining of spatial co-locations
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A framework of spatial co-location mining on MapReduce
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Incremental and parallel spatial association mining
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A Parallel Spatial Co-location Mining Algorithm Based on MapReduce
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Mining causal rules hidden in spatial co-locations based on dynamic spatial databases
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Spatial co-location pattern ordering
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Mining co-location patterns with spatial distribution characteristics
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Big Data Analytics for Emergency Communication Networks: A Survey
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A Spatial Maximal Co-location Pattern Mining Algorithm Based on Maximal Clique
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Cohesion based co-location pattern mining
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A framework for mining spatial high utility co-location patterns
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Incremental mining of co-locations from spatial database
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The Implementation of Spatial Co-location Mining and Its Application for POIs in Beijing
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Spatial co-location pattern discovery using multiple neighborhood relationship function
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A Review Study: Recognition of Co-located Pattern in Video Stream
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Mining Spatial Co-location Patterns by the Fuzzy Technology
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Discovering High Influence Co-location Patterns from Spatial Data Sets
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A Spatial Co-location Pattern Mining Algorithm Without Distance Thresholds
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Vector-Degree: A General Similarity Measure for Co-location Patterns
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Micro-Level Incident Analysis using Spatial Association Rule Mining
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Efficient Maximal Spatial Clique Enumeration
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Fraction-Score: A New Support Measure for Co-location Pattern Mining
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Mining At Most Top-K% Mixed-drove Spatio-temporal Co-occurrence Patterns: A Summary of Results
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Mixed-Drove Spatio-Temporal Co-occurence Pattern Mining: A Summary of Results
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Zonal Co-location Pattern Discovery with Dynamic Parameters
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Incremental Maintenance of Discovered Spatial Colocation Patterns
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Efficiently Mining Dynamic Zonal Co-location Patterns Based on Maximal Co-locations
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Incremental Maintenance of Topological Patterns in Spatial-Temporal Database
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Spatial Interestingness Measures for Co-location Pattern Mining
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Mining Spatio-temporal Patterns in the Presence of Concept Hierarchies
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