Lizhen Wang

Thanhcong Do

ACM Trans. Knowl. Discov. Data, January, 2024

Attribute-sensitive community search over attributed heterogeneous information networks.

[BibT_eX]

[DOI]

Expert Syst. Appl., January, 2024

2023

A spatial co-location pattern mining framework insensitive to prevalence thresholds based on overlapping cliques.

[BibT_eX]

[DOI]

Distributed Parallel Databases, December, 2023

Neighbor-aware deep multi-view clustering via graph convolutional network.

[BibT_eX]

[DOI]

Inf. Fusion, May, 2023

Efficiently mining maximal l-reachability co-location patterns from spatial data sets.

[BibT_eX]

[DOI]

Intell. Data Anal., 2023

Spatial co-location pattern mining over extended objects based on cell-relation operations.

[BibT_eX]

[DOI]

Expert Syst. Appl., 2023

Spatial-Aware Community Search Over Heterogeneous Information Networks.

[BibT_eX]

[DOI]

Proceedings of the Spatial Data and Intelligence - 4th International Conference, 2023

Continuous Sub-prevalent Co-location Pattern Mining.

[BibT_eX]

[DOI]

Qilong Wang

Proceedings of the Spatial Data and Intelligence - 4th International Conference, 2023

Influential Community Search Over Large Heterogeneous Information Networks.

[BibT_eX]

[DOI]

Proceedings of the Spatial Data and Intelligence - 4th International Conference, 2023

Fast Mining Prevalent Co-location Patterns Over Dense Spatial Datasets.

[BibT_eX]

[DOI]

Proceedings of the Spatial Data and Intelligence - 4th International Conference, 2023

A multi-view anomalous co-location detection framework considering both intra- and inter-feature couplings.

[BibT_eX]

[DOI]

Proceedings of the 24th IEEE International Conference on Mobile Data Management, 2023

ODSS-RCPM: An Online Decision Support System Based on Regional Co-location Pattern Mining.

[BibT_eX]

[DOI]

Dongsheng Wang

Proceedings of the Database Systems for Advanced Applications, 2023

Efficient Mining of High Utility Co-location Patterns Based on a Query Strategy.

[BibT_eX]

[DOI]

Proceedings of the Advanced Data Mining and Applications - 19th International Conference, 2023

2022

Mining Non-Redundant Co-Location Patterns.

[BibT_eX]

[DOI]

IEEE Trans. Neural Networks Learn. Syst., 2022

SCPM-CR: A Novel Method for Spatial Co-Location Pattern Mining With Coupling Relation Consideration.

[BibT_eX]

[DOI]

IEEE Trans. Knowl. Data Eng., 2022

Spatial Colocation Pattern Discovery Incorporating Fuzzy Theory.

[BibT_eX]

[DOI]

IEEE Trans. Fuzzy Syst., 2022

A maximal ordered ego-clique based approach for prevalent co-location pattern mining.

[BibT_eX]

[DOI]

Muquan Zou

Inf. Sci., 2022

Efficiently mining spatial co-location patterns utilizing fuzzy grid cliques.

[BibT_eX]

[DOI]

Inf. Sci., 2022

Mining Type-β Co-Location Patterns on Closeness Centrality in Spatial Data Sets.

[BibT_eX]

[DOI]

ISPRS Int. J. Geo Inf., 2022

Mining spatial high-average utility co-location patterns from spatial data sets.

[BibT_eX]

[DOI]

Intell. Data Anal., 2022

TCPMS-FCP: A Traffic Congestion Pattern Mining System Based on Spatio-Temporal Fuzzy Co-location Patterns.

[BibT_eX]

[DOI]

Proceedings of the Web Information Systems Engineering - WISE 2022, 2022

Information Diffusion Prediction Based on Deep Attention in Heterogeneous Networks.

[BibT_eX]

[DOI]

Proceedings of the Spatial Data and Intelligence - Third International Conference, 2022

Diversified Top-k Spatial Pattern Matching.

[BibT_eX]

[DOI]

Jiahua Xie

Proceedings of the Spatial Data and Intelligence - Third International Conference, 2022

Heterogeneous Network Representation Learning Based on Adaptive Multi-channel Graph Convolution.

[BibT_eX]

[DOI]

Proceedings of the Spatial Data and Intelligence - Third International Conference, 2022

MSIDP: Multi-scale Information Diffusion Prediction with Timestamp Information and Wide Dispersion.

[BibT_eX]

[DOI]

Proceedings of the International Joint Conference on Neural Networks, 2022

Mining fuzzy sub-prevalent co-location pattern with dominant feature.

[BibT_eX]

[DOI]

Proceedings of the 30th International Conference on Advances in Geographic Information Systems, 2022

Spatial Feature Evolutionary Relationship Discovery Based on Tree Structure Pattern Mining and Spatial Co-Location Pattern Mining in Spatial Cliques.

[BibT_eX]

[DOI]

Kaishuo Lin

Proceedings of the Fuzzy Systems and Data Mining VIII, 2022

OESCPM: An Online Extended Spatial Co-location Pattern Mining System.

[BibT_eX]

[DOI]

Proceedings of the Web and Big Data - 6th International Joint Conference, 2022

Discovering Prevalent Weighted Co-Location Patterns on Spatial Data Without Candidates.

[BibT_eX]

[DOI]

Proceedings of the Web and Big Data - 6th International Joint Conference, 2022

Mining ε-Closed High Utility Co-location Patterns from Spatial Data.

[BibT_eX]

[DOI]

Proceedings of the Advanced Data Mining and Applications - 18th International Conference, 2022

Mining the Potential Relationships Between Cancer Cases and Industrial Pollution Based on High-Influence Ordered-Pair Patterns.

[BibT_eX]

[DOI]

Proceedings of the Advanced Data Mining and Applications - 18th International Conference, 2022

Mining Maximal Sub-prevalent Co-location Patterns Based on k-hop.

[BibT_eX]

[DOI]

Yingbi Chen

Proceedings of the Advanced Data Mining and Applications - 18th International Conference, 2022

Preference-based Spatial Co-location Pattern Mining

[BibT_eX]

[DOI]

Yuan Fang

Springer, ISBN: 978-981-16-7565-2, 2022

2021

Efficient discovery of co-location patterns from massive spatial datasets with or without rare features.

[BibT_eX]

[DOI]

Knowl. Inf. Syst., 2021

High Influencing Pattern Discovery over Time Series Data.

[BibT_eX]

[DOI]

ISPRS Int. J. Geo Inf., 2021

A network-based sparse and multi-manifold regularized multiple non-negative matrix factorization for multi-view clustering.

[BibT_eX]

[DOI]

Expert Syst. Appl., 2021

MCHT: A maximal clique and hash table-based maximal prevalent co-location pattern mining algorithm.

[BibT_eX]

[DOI]

Expert Syst. Appl., 2021

Deep Multiple Auto-Encoder-Based Multi-view Clustering.

[BibT_eX]

[DOI]

Data Sci. Eng., 2021

Parallel Co-location Pattern Mining based on Neighbor-Dependency Partition and Column Calculation.

[BibT_eX]

[DOI]

Proceedings of the SIGSPATIAL '21: 29th International Conference on Advances in Geographic Information Systems, 2021

EHUCM: An Efficient Algorithm for Mining High Utility Co-location Patterns from Spatial Datasets with Feature-specific Utilities.

[BibT_eX]

[DOI]

Proceedings of the Database and Expert Systems Applications, 2021

Deep Attributed Network Embedding Based on the PPMI.

[BibT_eX]

[DOI]

Proceedings of the Database Systems for Advanced Applications. DASFAA 2021 International Workshops, 2021

NRCP-Miner: Towards the Discovery of Non-redundant Co-location Patterns.

[BibT_eX]

[DOI]

Proceedings of the Database Systems for Advanced Applications, 2021

2020

ESPM: Efficient Spatial Pattern Matching.

[BibT_eX]

[DOI]

IEEE Trans. Knowl. Data Eng., 2020

Delaunay triangulation-based spatial colocation pattern mining without distance thresholds.

[BibT_eX]

[DOI]

Stat. Anal. Data Min., 2020

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

[BibT_eX]

[DOI]

Lu Yang

Evol. Intell., 2020

Mining high influence co-location patterns from instances with attributes.

[BibT_eX]

[DOI]

Evol. Intell., 2020

A MapReduce approach for spatial co-location pattern mining via ordered-clique-growth.

[BibT_eX]

[DOI]

Distributed Parallel Databases, 2020

An efficient architecture for medical high-resolution images transmission in mobile telemedicine systems.

[BibT_eX]

[DOI]

Comput. Methods Programs Biomed., 2020

ESPM: Efficient Spatial Pattern Matching (Extended Abstract).

[BibT_eX]

[DOI]

Proceedings of the 36th IEEE International Conference on Data Engineering, 2020

The Coupling Co-Location Pattern: A New Spatial Pattern for Spatial Data Sets.

[BibT_eX]

[DOI]

Shiran Zhou

Proceedings of the Fuzzy Systems and Data Mining VI, 2020

Attributed Network Embedding with Community Preservation.

[BibT_eX]

[DOI]

Proceedings of the 7th IEEE International Conference on Data Science and Advanced Analytics, 2020

Multi-view Clustering via Multiple Auto-Encoder.

[BibT_eX]

[DOI]

Proceedings of the Web and Big Data - 4th International Joint Conference, 2020

2019

Mining maximal sub-prevalent co-location patterns.

[BibT_eX]

[DOI]

World Wide Web, 2019

A tensor framework for geosensor data forecasting of significant societal events.

[BibT_eX]

[DOI]

Pattern Recognit., 2019

Spatial Co-location Pattern Mining Based on Fuzzy Neighbor Relationship.

[BibT_eX]

[DOI]

Meijiao Wang

Lihong Zhao

J. Inf. Sci. Eng., 2019

A clique-based approach for co-location pattern mining.

[BibT_eX]

[DOI]

Inf. Sci., 2019

An Effective Approach on Mining Co-Location Patterns from Spatial Databases with Rare Features.

[BibT_eX]

[DOI]

Proceedings of the 20th IEEE International Conference on Mobile Data Management, 2019

Prevalent Co-Visiting Patterns Mining from Location-Based Social Networks.

[BibT_eX]

[DOI]

Proceedings of the 20th IEEE International Conference on Mobile Data Management, 2019

Mining Prevalent Co-Location Patterns Based on Global Topological Relations.

[BibT_eX]

[DOI]

Jialong Wang

Xiaoxu Wang

Proceedings of the 20th IEEE International Conference on Mobile Data Management, 2019

Mining Spatial Co-Location Patterns Based on Overlap Maximal Clique Partitioning.

[BibT_eX]

[DOI]

Proceedings of the 20th IEEE International Conference on Mobile Data Management, 2019

Mining Significant Co-Location Patterns From Spatial Regional Objects.

[BibT_eX]

[DOI]

yurong Long

Proceedings of the 20th IEEE International Conference on Mobile Data Management, 2019

POI Representation Learning by a Hybrid Model.

[BibT_eX]

[DOI]

Proceedings of the 20th IEEE International Conference on Mobile Data Management, 2019

Vector-Degree: A General Similarity Measure for Co-location Patterns.

[BibT_eX]

[DOI]

Muquan Zou

Proceedings of the 2019 IEEE International Conference on Big Knowledge, 2019

A Spatial Co-location Pattern Mining Algorithm Without Distance Thresholds.

[BibT_eX]

[DOI]

Proceedings of the 2019 IEEE International Conference on Big Knowledge, 2019

Mining Spatial Co-location Patterns by the Fuzzy Technology.

[BibT_eX]

[DOI]

Le Lei

Proceedings of the 2019 IEEE International Conference on Big Knowledge, 2019

Spatial Colocation Pattern Mining with the Maximum Membership Threshold.

[BibT_eX]

[DOI]

Meijiao Wang

Proceedings of the Fuzzy Systems and Data Mining V, 2019

Incremental Mining of Spatial Co-Location Patterns Based on the Fuzzy Neighborhood Relationship.

[BibT_eX]

[DOI]

Proceedings of the Fuzzy Systems and Data Mining V, 2019

Discovering Spatial Co-Location Patterns by Automatically Determining the Instance Neighbor.

[BibT_eX]

[DOI]

Proceedings of the Fuzzy Systems and Data Mining V, 2019

Multi-View Clustering via Nonnegative Matrix Factorization with L<sub>21</sub> Norm.

[BibT_eX]

[DOI]

Proceedings of the Fuzzy Systems and Data Mining V, 2019

2018

Redundancy Reduction for Prevalent Co-Location Patterns.

[BibT_eX]

[DOI]

IEEE Trans. Knowl. Data Eng., 2018

Effective lossless condensed representation and discovery of spatial co-location patterns.

[BibT_eX]

[DOI]

Inf. Sci., 2018

A Parallel Joinless Algorithm for Co-location Pattern Mining Based on Group-Dependent Shard.

[BibT_eX]

[DOI]

Proceedings of the Web Information Systems Engineering - WISE 2018, 2018

An Incremental Approach Based on the Coalition Formation Game Theory for Identifying Communities in Dynamic Social Networks.

[BibT_eX]

[DOI]

Proceedings of the Knowledge Science, Engineering and Management, 2018

A Business Site Selection System Using Co-Locations and Ontologies.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Parallel & Distributed Processing with Applications, 2018

A Co-Location-Based Approach for Business Site Selection Using Ontologies.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Parallel & Distributed Processing with Applications, 2018

Interactive Probabilistic Post-Mining of User-Preferred Spatial Co-Location Patterns.

[BibT_eX]

[DOI]

Longbing Cao

Proceedings of the 34th IEEE International Conference on Data Engineering, 2018

Basic and Optimization Algorithms for Mining High Utility Co-Location Patterns from Fuzzy Datasets.

[BibT_eX]

[DOI]

Yubing Chen

Jialong Wang

Proceedings of the Fuzzy Systems and Data Mining IV, 2018

A Parallel Spatial Co-location Pattern Mining Approach Based on Ordered Clique Growth.

[BibT_eX]

[DOI]

Proceedings of the Database Systems for Advanced Applications, 2018

A Tensor-Based Method for Geosensor Data Forecasting.

[BibT_eX]

[DOI]

Proceedings of the Web and Big Data - Second International Joint Conference, 2018

TSRS: Trip Service Recommended System Based on Summarized Co-location Patterns.

[BibT_eX]

[DOI]

Tao Zhang

Proceedings of the Web and Big Data - Second International Joint Conference, 2018

DFCPM: A Dominant Feature Co-location Pattern Miner.

[BibT_eX]

[DOI]

Proceedings of the Web and Big Data - Second International Joint Conference, 2018

Multivariate Time Series Clustering via Multi-relational Community Detection in Networks.

[BibT_eX]

[DOI]

Proceedings of the Web and Big Data - Second International Joint Conference, 2018

Measuring the Spatio-Temporal Similarity Between Users.

[BibT_eX]

[DOI]

Proceedings of the Web and Big Data, 2018

2017

Spatial Co-Location Pattern Discovery from Fuzzy Objects.

[BibT_eX]

[DOI]

Zhiping Ouyang

Int. J. Artif. Intell. Tools, 2017

Maximal Sub-prevalent Co-location Patterns and Efficient Mining Algorithms.

[BibT_eX]

[DOI]

Proceedings of the Web Information Systems Engineering - WISE 2017, 2017

Mining Co-location Patterns with Dominant Features.

[BibT_eX]

[DOI]

Proceedings of the Web Information Systems Engineering - WISE 2017, 2017

Game influence diagrams with interval-valued utilities.

[BibT_eX]

[DOI]

Proceedings of the 13th International Conference on Natural Computation, 2017

Mining High Utility Co-location Patterns Based on Importance of Spatial Region.

[BibT_eX]

[DOI]

Proceedings of the Geo-Spatial Knowledge and Intelligence - 5th International Conference, 2017

UMine: Study on Prevalent Co-locations Mining from Uncertain Data Sets.

[BibT_eX]

[DOI]

Proceedings of the Geo-Spatial Knowledge and Intelligence - 5th International Conference, 2017

Efficiently Mining High Utility Co-location Patterns from Spatial Data Sets with Instance-Specific Utilities.

[BibT_eX]

[DOI]

Proceedings of the Database Systems for Advanced Applications, 2017

Discovering Interesting Co-location Patterns Interactively Using Ontologies.

[BibT_eX]

[DOI]

Proceedings of the Database Systems for Advanced Applications, 2017

Incremental mining of high utility co-locations from spatial database.

[BibT_eX]

[DOI]

Proceedings of the 2017 IEEE International Conference on Big Data and Smart Computing, 2017

OICPM: An Interactive System to Find Interesting Co-location Patterns Using Ontologies.

[BibT_eX]

[DOI]

Qing Xiao

Proceedings of the Web and Big Data - First International Joint Conference, 2017

2016

一种基于滑动窗口模式匹配的加权预测方法 (Weighted Prediction Method Based on Sliding Window and Pattern Matching).

[BibT_eX]

[DOI]

Shikun Deng

计算机科学, 2016

Naïve Bayesian Classification of Uncertain Objects Based on the Theory of Interval Probability.

[BibT_eX]

[DOI]

Weiyi Liu

Int. J. Artif. Intell. Tools, 2016

Top-k probabilistic prevalent co-location mining in spatially uncertain data sets.

[BibT_eX]

[DOI]

Frontiers Comput. Sci., 2016

Effectively Updating High Utility Co-location Patterns in Evolving Spatial Databases.

[BibT_eX]

[DOI]

Proceedings of the Web-Age Information Management - 17th International Conference, 2016

Mining co-location patterns with spatial distribution characteristics.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Computer, 2016

Spatial co-location pattern ordering.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Computer, 2016

Mining top-k-size maximal co-location patterns.

[BibT_eX]

[DOI]

Jiasong Zhao

Proceedings of the International Conference on Computer, 2016

A novel method on incremental mining of spatial co-locations.

[BibT_eX]

[DOI]

Proceedings of the 2016 International Conference on Big Data and Smart Computing, 2016

An Improved ML-kNN Approach Based on Coupled Similarity.

[BibT_eX]

[DOI]

Xiaodan Yang

Proceedings of the Web Technologies and Applications, 2016

Co-location Detector: A System to Find Interesting Spatial Co-locating Relationships.

[BibT_eX]

[DOI]

Qing Xiao

Proceedings of the Web Technologies and Applications - 18th Asia-Pacific Web Conference, 2016

OICRM: An Ontology-Based Interesting Co-location Rule Miner.

[BibT_eX]

[DOI]

Meijiao Wang

Proceedings of the Web Technologies and Applications - 18th Asia-Pacific Web Conference, 2016

Ontology-Based Interactive Post-mining of Interesting Co-location Patterns.

[BibT_eX]

[DOI]

Proceedings of the Web Technologies and Applications - 18th Asia-Pacific Web Conference, 2016

2015

An approach for overlapping and hierarchical community detection in social networks based on coalition formation game theory.

[BibT_eX]

[DOI]

Expert Syst. Appl., 2015

CDSG: A Community Detection System Based on the Game Theory.

[BibT_eX]

[DOI]

Proceedings of the Web-Age Information Management - 16th International Conference, 2015

SQNR: A System for Querying Nodes and relations in multi-relational social networks.

[BibT_eX]

[DOI]

Proceedings of the 12th International Conference on Fuzzy Systems and Knowledge Discovery, 2015

A framework for mining spatial high utility co-location patterns.

[BibT_eX]

[DOI]

Proceedings of the 12th International Conference on Fuzzy Systems and Knowledge Discovery, 2015

A Fast Approach for Detecting Overlapping Communities in Social Networks Based on Game Theory.

[BibT_eX]

[DOI]

Proceedings of the Data Science - 30th British International Conference on Databases, 2015

2014

Finding associations-between-groups in multimode networks.

[BibT_eX]

[DOI]

Proceedings of the 2014 International Conference on Behavioral, 2014

2013

Finding Probabilistic Prevalent Colocations in Spatially Uncertain Data Sets.

[BibT_eX]

[DOI]

IEEE Trans. Knowl. Data Eng., 2013

Extracting Prevalent Co-location Patterns from Historic Spatial Data.

[BibT_eX]

[DOI]

Proceedings of the Web-Age Information Management, 2013

Mining Co-locations from Spatially Uncertain Data with Probability Intervals.

[BibT_eX]

[DOI]

Proceedings of the Web-Age Information Management, 2013

2011

Static Strategic Game Approach for Multiple Attribute Decision Making Problems without Weight Information.

[BibT_eX]

[DOI]

Weiyi Liu

Int. J. Artif. Intell. Tools, 2011

2010

An efficient method of evaluating the distance between two uncertain objects.

[BibT_eX]

[DOI]

Proceedings of the 8th IEEE International Conference on Control and Automation, 2010

Frequent patterns-based subspace clustering.

[BibT_eX]

[DOI]

Yue Jiang

Proceedings of the Seventh International Conference on Fuzzy Systems and Knowledge Discovery, 2010

Efficiently Mining Co-Location Rules on Interval Data.

[BibT_eX]

[DOI]

Proceedings of the Advanced Data Mining and Applications - 6th International Conference, 2010

Evaluating the Distance between Two Uncertain Categorical Objects.

[BibT_eX]

[DOI]

Proceedings of the Advanced Data Mining and Applications - 6th International Conference, 2010

2009

An order-clique-based approach for mining maximal co-locations.

[BibT_eX]

[DOI]

Inf. Sci., 2009

Influence Diagram Model with Interval-Valued Utilities.

[BibT_eX]

[DOI]

Weiyi Liu