On the public citation graph

Mixed-Drove Spatiotemporal Co-Occurrence Pattern Mining

https://doi.org/10.1109/tkde.2008.97

0 signed · 113 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.

Claim with ORCID

Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.

See reference health

What this work says

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

inferredcites →https://doi.org/10.1002/0471725218openalex-snapshot

Spatial Statistics

inferredcites →https://doi.org/10.1002/9781119115151openalex-snapshot

Statistics for Spatial Data

inferredcites →https://doi.org/10.1002/series1345openalex-snapshot

Wiley Series in Probability and Statistics

inferredcites →https://doi.org/10.1007/11535331_21openalex-snapshot

On Discovering Moving Clusters in Spatio-temporal Data

inferredcites →https://doi.org/10.1007/11760146_54openalex-snapshot

Spatial-Temporal Cross-Correlation Analysis: A New Measure and a Case Study in Infectious Disease Informatics

inferredcites →https://doi.org/10.1007/3-540-26772-7_16openalex-snapshot

Finding REMO — Detecting Relative Motion Patterns in Geospatial Lifelines

inferredcites →https://doi.org/10.1007/3-540-45799-2_10openalex-snapshot

Analyzing Relative Motion within Groups ofTrackable Moving Point Objects

inferredcites →https://doi.org/10.1007/3-540-47724-1_13openalex-snapshot

Discovering Spatial Co-location Patterns: A Summary of Results

inferredcites →https://doi.org/10.1007/978-3-319-17885-1_100805openalex-snapshot

Moving Object Databases

inferredcites →https://doi.org/10.1007/978-3-540-45081-8_9openalex-snapshot

Chapter 9: Spatio-temporal Databases in the Years Ahead

inferredcites →https://doi.org/10.1007/b83622openalex-snapshot

Spatio-Temporal Databases: The CHOROCHRONOS Approach

inferredcites →https://doi.org/10.1007/s10707-005-4574-9openalex-snapshot

Mobility Patterns

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/10.1109/icdm.2006.112openalex-snapshot

Mixed-Drove Spatio-Temporal Co-occurence Pattern Mining: A Summary of Results

inferredcites →https://doi.org/10.1109/icdm.2006.59openalex-snapshot

Discovery of Collocation Episodes in Spatiotemporal Data

inferredcites →https://doi.org/10.1109/tkde.2004.90openalex-snapshot

Discovering colocation patterns from spatial data sets: a general approach

inferredcites →https://doi.org/10.1109/tkde.2006.150openalex-snapshot

A Joinless Approach for Mining Spatial Colocation Patterns

inferredcites →https://doi.org/10.1109/tkde.2008.97openalex-snapshot

Mixed-Drove Spatiotemporal Co-Occurrence Pattern Mining

inferredcites →https://doi.org/10.1111/1467-9868.00315openalex-snapshot

Spatiotemporal Prediction for Log-Gaussian Cox Processes

inferredcites →https://doi.org/10.1111/j.1365-3121.1992.tb00605.xopenalex-snapshot

STATISTICS FOR SPATIAL DATA

inferredcites →https://doi.org/10.1145/1014052.1014095openalex-snapshot

Fast mining of spatial collocations

inferredcites →https://doi.org/10.1145/1032222.1032258openalex-snapshot

A partial join approach for mining co-location patterns

inferredcites →https://doi.org/10.1145/1032222.1032259openalex-snapshot

Efficient detection of motion patterns in spatio-temporal data sets

inferredcites →https://doi.org/10.1145/1081870.1081962openalex-snapshot

A generalized framework for mining spatio-temporal patterns in scientific data

inferredcites →https://doi.org/10.1145/1099554.1099680openalex-snapshot

A framework for mining topological patterns in spatio-temporal databases

inferredcites →https://doi.org/10.1145/1183471.1183479openalex-snapshot

Computing longest duration flocks in trajectory data

inferredcites →https://doi.org/10.1201/9780203487808openalex-snapshot

Hierarchical Modeling and Analysis for Spatial Data

inferredcites →https://doi.org/10.1201/9781315275086openalex-snapshot

Statistical Methods for Spatial Data Analysis

inferredcites →https://doi.org/10.1201/b17115openalex-snapshot

Hierarchical Modeling and Analysis for Spatial Data

inferredcites →https://doi.org/10.1257/0002828041464669openalex-snapshot

Time Series Analysis, Cointegration, and Applications

inferredcites →https://doi.org/10.2307/1269015openalex-snapshot

Time Series Analysis: Univariate and Multivariate Methods

inferredcites →https://doi.org/10.2307/2982871openalex-snapshot

Statistics for Spatial Data.

inferredcites →https://doi.org/10.4324/9780203491980openalex-snapshot

Statistical Methods for Spatial Data Analysis

What points at this work

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

inferred← citeshttps://doi.org/10.1002/widm.1151openalex-snapshot

A Delaunay diagram‐based Min–Max CP‐Tree algorithm for Spatial Data Analysis

inferred← citeshttps://doi.org/10.1002/widm.25openalex-snapshot

Identifying patterns in spatial information: A survey of methods

inferred← citeshttps://doi.org/10.1007/978-3-030-04780-1_29openalex-snapshot

Spatial Co-location Pattern Mining

inferred← citeshttps://doi.org/10.1007/978-3-030-82472-3_3openalex-snapshot

Maximal Mixed-Drove Co-Occurrence Patterns

inferred← citeshttps://doi.org/10.1007/978-3-031-32910-4_14openalex-snapshot

Continuous Sub-prevalent Co-location Pattern Mining

inferred← citeshttps://doi.org/10.1007/978-3-319-17885-1_1552openalex-snapshot

Statistically Significant Co-location Pattern Mining

inferred← citeshttps://doi.org/10.1007/978-3-319-23519-6_1552-1openalex-snapshot

Statistically Significant Co-location Pattern Mining

inferred← citeshttps://doi.org/10.1007/978-3-319-60195-3_2openalex-snapshot

Spatial and Spatiotemporal Big Data Science

inferred← citeshttps://doi.org/10.1007/978-3-319-99873-2_3openalex-snapshot

Modeling Spatiotemporal Relationships Among Trajectories

inferred← citeshttps://doi.org/10.1007/978-3-642-22555-0_55openalex-snapshot

A New Data Mining Approach to Find Co-location Pattern from Spatial Data

inferred← citeshttps://doi.org/10.1007/978-3-642-22922-0_2openalex-snapshot

SSCP: Mining Statistically Significant Co-location Patterns

inferred← citeshttps://doi.org/10.1007/978-3-642-23091-2_10openalex-snapshot

How to Use ”Classical” Tree Mining Algorithms to Find Complex Spatio-Temporal Patterns?

inferred← citeshttps://doi.org/10.1007/978-3-642-30220-6_14openalex-snapshot

The Pattern Next Door: Towards Spatio-sequential Pattern Discovery

inferred← citeshttps://doi.org/10.1007/978-3-642-33460-3_31openalex-snapshot

Graph Mining for Object Tracking in Videos

inferred← citeshttps://doi.org/10.1007/978-3-642-41154-0_29openalex-snapshot

On Co-occurrence Pattern Discovery from Spatio-temporal Event Stream

inferred← citeshttps://doi.org/10.1007/978-981-15-9774-9_42openalex-snapshot

Evaluation Metrics of Spatial and Spatiotemporal Data Mining Techniques

inferred← citeshttps://doi.org/10.1007/978-981-33-4604-8_18openalex-snapshot

A Survey on Spatiotemporal Co-occurrence Pattern Mining Techniques

inferred← citeshttps://doi.org/10.1007/978-981-95-8318-8_9openalex-snapshot

Sustained Evolving Co-occurrence Pattern Mining

inferred← citeshttps://doi.org/10.1007/s00354-024-00276-zopenalex-snapshot

A Co-occurrence Prediction Framework in Location-Based Social Networks

inferred← citeshttps://doi.org/10.1007/s10109-015-0216-4openalex-snapshot

A generic regional spatio-temporal co-occurrence pattern mining model: a case study for air pollution

inferred← citeshttps://doi.org/10.1007/s10115-014-0750-2openalex-snapshot

Partial spatio-temporal co-occurrence pattern mining

inferred← citeshttps://doi.org/10.1007/s10115-020-01478-9openalex-snapshot

Mining evolutions of complex spatial objects using a single-attributed Directed Acyclic Graph

inferred← citeshttps://doi.org/10.1007/s10115-025-02663-4openalex-snapshot

Discovering regional congestion propagation patterns based on spatio-temporal co-location patterns

inferred← citeshttps://doi.org/10.1007/s10462-021-09994-yopenalex-snapshot

Spatiotemporal data mining: a survey on challenges and open problems

inferred← citeshttps://doi.org/10.1007/s10707-016-0255-0openalex-snapshot

Mining spatiotemporal co-occurrence patterns in non-relational databases

inferred← citeshttps://doi.org/10.1007/s10707-023-00506-4openalex-snapshot

Discovery of multi-domain spatiotemporal associations

inferred← citeshttps://doi.org/10.1007/s10796-022-10344-8openalex-snapshot

Maximal Mixed-Drove Co-occurrence Patterns

inferred← citeshttps://doi.org/10.1007/s11042-018-6752-4openalex-snapshot

An innovative model to mine asynchronous periodic pattern of moving objects

inferred← citeshttps://doi.org/10.1007/s11280-018-0578-xopenalex-snapshot

CoPFun: an urban co-occurrence pattern mining scheme based on regional function discovery

inferred← citeshttps://doi.org/10.1007/s11704-013-3902-8openalex-snapshot

Co-occurrence prediction in a large location-based social network

inferred← citeshttps://doi.org/10.1007/s12065-019-00332-4openalex-snapshot

Mining traffic congestion propagation patterns based on spatio-temporal co-location patterns

inferred← citeshttps://doi.org/10.1007/s13762-015-0880-4openalex-snapshot

Identification of air pollution patterns using a modified fuzzy co-occurrence pattern mining method

inferred← citeshttps://doi.org/10.1007/s41019-026-00347-9openalex-snapshot

Mining Sustained Emerging Spatio-Temporal Co-occurrence Patterns from Dynamic Spatial Databases

inferred← citeshttps://doi.org/10.1016/b978-0-12-409548-9.09594-4openalex-snapshot

Spatial and Spatiotemporal Data Mining

inferred← citeshttps://doi.org/10.1016/j.datak.2023.102153openalex-snapshot

Bounding box representation of co-location instances for Chebyshev and Manhattan metrics

inferred← citeshttps://doi.org/10.1016/j.eswa.2017.05.068openalex-snapshot

Discovering socially important locations of social media users

inferred← citeshttps://doi.org/10.1016/j.eswa.2025.129775openalex-snapshot

Mining spatiotemporal dominant co-location patterns

inferred← citeshttps://doi.org/10.1016/j.ins.2026.123148openalex-snapshot

Generalized mining of mixed drove co-occurrence patterns

inferred← citeshttps://doi.org/10.1016/j.proeng.2012.06.332openalex-snapshot

Discovering Co-location Patterns from Spatial Domain using a Delaunay Approach

inferred← citeshttps://doi.org/10.1080/13658816.2019.1566549openalex-snapshot

Mining spatiotemporal association patterns from complex geographic phenomena

inferred← citeshttps://doi.org/10.1080/13658816.2026.2720887openalex-snapshot

An event-based model for discovering frequent spatio-temporal patterns: application to the study of tropical cyclones

inferred← citeshttps://doi.org/10.1080/15230406.2019.1641149openalex-snapshot

Space-time cluster detection with cross-space-time relative risk functions

inferred← citeshttps://doi.org/10.1109/ascc.2013.6606379openalex-snapshot

Mining at most top-K% mixed-drove spatiotemporal co-occurrence patterns

inferred← citeshttps://doi.org/10.1109/cogsima.2019.8724300openalex-snapshot

Situation Mining: Event Pattern Mining for Situation Model Induction

inferred← citeshttps://doi.org/10.1109/icde48307.2020.00077openalex-snapshot

Multiscale Frequent Co-movement Pattern Mining

inferred← citeshttps://doi.org/10.1109/icdmw.2011.99openalex-snapshot

Incremental Maintenance of Topological Patterns in Spatial-Temporal Database

inferred← citeshttps://doi.org/10.1109/icdmw.2016.0162openalex-snapshot

A Pattern Growth-Based Approach for Mining Spatiotemporal Co-occurrence Patterns

inferred← citeshttps://doi.org/10.1109/icsai57119.2022.10005536openalex-snapshot

Research on Algorithms of Positive and Negative Co-occurrence in Spatio-temporal Datasets

inferred← citeshttps://doi.org/10.1109/icsdm.2011.5969016openalex-snapshot

Discovering partial spatio-temporal co-occurrence patterns

inferred← citeshttps://doi.org/10.1109/icsssm.2016.7538631openalex-snapshot

Detecting individual content-structure patterns in time series data

inferred← citeshttps://doi.org/10.1109/icuems50872.2020.00074openalex-snapshot

Apriori-based Spatial Pattern Mining Algorithm for Big Data

inferred← citeshttps://doi.org/10.1109/ihmsc.2018.00018openalex-snapshot

Ships' Spatiotemporal Co-Occurrence Patterns Mining Based on Sliding Spatiotemporal Cuboid and Improved Support

inferred← citeshttps://doi.org/10.1109/inista.2012.6247044openalex-snapshot

Mining periodic spatio-temporal co-occurrence patterns: A summary of results

inferred← citeshttps://doi.org/10.1109/tcss.2018.2883582openalex-snapshot

COOC: Visual Exploration of Co-Occurrence Mobility Patterns in Urban Scenarios

inferred← citeshttps://doi.org/10.1109/tits.2020.2983226openalex-snapshot

Towards Better Detection and Analysis of Massive Spatiotemporal Co-Occurrence Patterns

inferred← citeshttps://doi.org/10.1109/tkde.2011.146openalex-snapshot

Cascading Spatio-Temporal Pattern Discovery

inferred← citeshttps://doi.org/10.1109/tkde.2017.2754252openalex-snapshot

A Non-Parametric Algorithm for Discovering Triggering Patterns of Spatio-Temporal Event Types

inferred← citeshttps://doi.org/10.1109/tkde.2023.3241213openalex-snapshot

MCoR-Miner: Maximal Co-Occurrence Nonoverlapping Sequential Rule Mining

inferred← citeshttps://doi.org/10.1109/tvcg.2018.2851227openalex-snapshot

COPE: Interactive Exploration of Co-Occurrence Patterns in Spatial Time Series

inferred← citeshttps://doi.org/10.1137/1.9781611972801.29openalex-snapshot

Cascading spatio-temporal pattern discovery: A summary of results

inferred← citeshttps://doi.org/10.1142/s0218194017500140openalex-snapshot

Semantic Trajectory Knowledge Discovery: A Promising Way to Extract Meaningful Patterns from Spatiotemporal Data

inferred← citeshttps://doi.org/10.1145/2505515.2507820openalex-snapshot

Motif discovery in spatial trajectories using grammar inference

inferred← citeshttps://doi.org/10.1145/2618243.2618264openalex-snapshot

Managing evolving shapes in sensor networks

inferred← citeshttps://doi.org/10.1145/3004725.3004735openalex-snapshot

Discovering spatiotemporal event sequences

inferred← citeshttps://doi.org/10.1145/3139351openalex-snapshot

Measuring the Significance of Spatiotemporal Co-Occurrences

inferred← citeshttps://doi.org/10.1145/3557989.3566158openalex-snapshot

Towards geographically robust statistically significant regional colocation pattern detection

inferred← citeshttps://doi.org/10.1145/3748777.3748790openalex-snapshot

Discovering Super-Colocation Patterns: A Summary of Results

inferred← citeshttps://doi.org/10.1145/3764914.3770594openalex-snapshot

Towards Recurring Co-traveling Pattern Detection: A Summary of Results

inferred← citeshttps://doi.org/10.1504/ijcat.2013.056022openalex-snapshot

A multiple window-based co-location pattern mining approach for various types of spatial data

inferred← citeshttps://doi.org/10.3166/ria.27.619-648openalex-snapshot

Motifs spatio-temporels. Enjeux et applications à l'environnement

inferred← citeshttps://doi.org/10.3233/ida-160806openalex-snapshot

Spatio-sequential patterns mining: Beyond the boundaries

inferred← citeshttps://doi.org/10.3390/ijgi12070261openalex-snapshot

A Novel Approach for Mining Spatiotemporal Explicit and Implicit Information in Multiscale Spatiotemporal Data

inferred← citeshttps://doi.org/10.3390/ijgi4042306openalex-snapshot

Spatiotemporal Data Mining: A Computational Perspective

inferred← citeshttps://doi.org/10.3390/jmse11030528openalex-snapshot

Spatiotemporal Companion Pattern (STCP) Mining of Ships Based on Trajectory Features

inferred← citeshttps://doi.org/10.4018/ijagr.298300openalex-snapshot

Clustering-Assisted Regional Spatio-Temporal Sequence Pattern Mining in Crime Database

inferred← citeshttps://doi.org/10.5121/ijdms.2011.3311openalex-snapshot

Event Centric Modeling Approach in Colocation Pattern Analysis From Spatial Data

inferred← citeshttps://doi.org/10.5194/isprs-annals-iv-4-w4-197-2017openalex-snapshot

FAST SS-ILM: A COMPUTATIONALLY EFFICIENT ALGORITHM TO DISCOVER SOCIALLY IMPORTANT LOCATIONS

inferred← citeshttps://doi.org/10.70675/f7c24167ze759z4fecz97f5z8aa641dfc7f6openalex-snapshot

Contribution à la fouille de données spatio-temporelles : application à l'étude de l'érosion

inferred← citeshttps://doi.org/10.7939/r3bc3t540openalex-snapshot

Statistically Sound Interaction Pattern Discovery from Spatial Data

showing the first 79 of 80 · page through all 80

Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.