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Lizhen Wang - One of the best experts on this subject based on the ideXlab platform.
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A MapReduce approach for spatial co-Location Pattern mining via ordered-clique-growth
Distributed and Parallel Databases, 2019Co-Authors: Peizhong Yang, Lizhen Wang, Xiaoxuan WangAbstract:Spatial co-Location Pattern is a subset of spatial features whose instances are frequently located together in geography. Mining co-Location Patterns are particularly valuable for discovering spatial dependencies. Traditional co-Location Pattern mining algorithms are computationally expensive with rapidly increasing of data volume. In this paper, we explore a novel iterative framework based on parallel ordered-clique-growth for co-Location Pattern mining. The ordered clique extension can re-use previously processed information and be executed in parallel, and hence speed up the identification of co-Location instances. Based on the iterative framework, a MapReduce algorithm is designed to search for prevalent co-Location Patterns in a level-wise manner, namely PCPM_OC. To narrow the search space of ordered cliques, two pruning techniques are suggested for filtering invalid clique instances as much as possible. The completeness and correctness of PCPM_OC are proven and we also discuss its complexity in this paper. Moreover, we compare PCPM_OC with two advanced MapReduce based co-Location Pattern mining algorithms on multiple perspectives. At last, substantial experiments are conducted on synthetic and real-world spatial datasets to study the performance of PCPM_OC. Experimental results demonstrate that PCPM_OC has a significant improvement in efficiency and shows better scalability on massive spatial data.
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Mining high influence co-Location Patterns from instances with attributes
Evolutionary Intelligence, 2019Co-Authors: Dianwu Fang, Lizhen Wang, Peizhong Yang, Lan ChenAbstract:A spatial co-Location Pattern describes coexistence of spatial features whose instances frequently appear together in geographic space. Numerous studies have been proposed to discover interesting co-Location Patterns from spatial data sets, but most of them only use the Location information of instances. As a result, they cannot adequately reflect the influence between instances. In this paper, we take additional attributes of instances into account in the process of co-Location Pattern mining, and propose a new approach for discovering the high influence co-Location Patterns. In our approach, we consider the spatial neighboring relationships and the similarity of instances simultaneously, and utilize the information entropy approach to measure the influence of any instance exerting on its neighbors and the influence of any feature in a co-Location Pattern. Then, an influence index for measuring the interestingness of a co-Location Pattern is proposed and we prove the influence index measure satisfies the downward closure property that can be used for pruning the search space, and thus an efficient high influence co-Location Pattern mining algorithm is designed. At last, extensive experiments are conducted on synthetic and real spatial data sets. Experimental results reveal the effectiveness and efficiency of our method.
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ICBK - Mining Spatial Co-Location Patterns by the Fuzzy Technology
2019 IEEE International Conference on Big Knowledge (ICBK), 2019Co-Authors: Lizhen Wang, Xiaoxuan WangAbstract:The main purpose of co-Location Pattern mining is to mine the set of spatial features whose instances are frequently located together in space. Because a single distance threshold is chosen in the previous methods when generating the neighbourhood relationships, some interesting spatial coLocation Patterns can't be extracted. In addition, previous methods don't take the neighborhood degree into consideration and they depend upon the PI (participation index) to measure the prevalence of the co-Locations, which these methods are very sensitive to PI and also lead to the absence of co-Location Patterns. In order to overcome these limitations of traditional co-Location Pattern mining, considering that the neighbor relationship is a fuzzy concept, this paper introduces the fuzzy theory into co-Location Pattern mining, a new fuzzy spatial neighborhood relationship measurement between instances and a reasonable feature proximity measurement between spatial features are proposed. Then, a novel algorithm based on fuzzy C-medoids clustering algorithm, FCB, is proposed, extensive experiments on synthetic and real-world data sets prove the practicability and efficiency of the proposed mining algorithm, it also proves that the algorithm has low sensitivity to thresholds and has high robustness.
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ICBK - Discovering High Influence Co-Location Patterns from Spatial Data Sets
2019 IEEE International Conference on Big Knowledge (ICBK), 2019Co-Authors: Lizhen Wang, Yuming Zeng, Lanqing ZengAbstract:The co-Location Pattern is a subset of spatial features that are frequently located together in spatial proximity. However, the traditional approaches only focus on the prevalence of Patterns, and it cannot reflect the influence of Patterns. In this paper, we are committed to address the problem of mining high influence co-Location Patterns. At first, we define the concepts of influence features and reference features. Based on these concepts, a series of definitions are introduced further to describe the influence co-Location Pattern. Secondly, a metric is designed to measure the influence degree of the influence co-Location Pattern, and a basic algorithm for mining high influence co-Location Patterns is presented. Then, according to the properties of the influence co-Location Pattern, the corresponding pruning strategy is proposed to improve the efficiency of the algorithm. At last, we conduct extensive experiments on synthetic and real data sets to test our approaches. Experimental results show that our approaches are effective and efficient to discover high influence co-Location Patterns.
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ICBK - Vector-Degree: A General Similarity Measure for Co-Location Patterns
2019 IEEE International Conference on Big Knowledge (ICBK), 2019Co-Authors: Pingping Wu, Lizhen WangAbstract:Co-Location Pattern mining is one of the hot issues in spatial Pattern mining. Similarity measures between co-Location Patterns can be used to solve problems such as Pattern compression, Pattern summarization, Pattern selection and Pattern ordering. Although, many researchers have focused on this issue recently and provided a more concise set of co-Location Patterns based on these measures. Unfortunately, these measures suffer from various weaknesses, e.g., some measures can only calculate the similarity between super-Pattern and sub-Pattern while some others require additional domain knowledge. In this paper, we propose a general similarity measure for any two co-Location Patterns. Firstly, we study the characteristics of the co-Location Pattern and present a novel representation model based on maximal cliques. Then, two materializations of the maximal clique and the Pattern relationship, 0-1 vector and key-value vector, are proposed and discussed in the paper. Moreover, based on the materialization methods, the similarity measure, Vector-Degree, is defined by applying the cosine similarity. Finally, similarity is used to group the Patterns by a hierarchical clustering algorithm. The experimental results on both synthetic and real world data sets show the efficiency and effectiveness of our proposed method.
Shashi Shekhar - One of the best experts on this subject based on the ideXlab platform.
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GIScience - Local Co-Location Pattern Detection: A Summary of Results
2018Co-Authors: Yan Li, Shashi ShekharAbstract:Given a set of spatial objects of different features (e.g., mall, hospital) and a spatial relation (e.g., geographic proximity), the problem of local co-Location Pattern detection (LCPD) pairs co-Location Patterns and localities such that the co-Location Patterns tend to exist inside the paired localities. A co-Location Pattern is a set of spatial features, the objects of which are often related to each other. Local co-Location Patterns are common in many fields, such as public security, and public health. For example, assault crimes and drunk driving events co-locate near bars. The problem is computationally challenging because of the exponential number of potential co-Location Patterns and candidate localities. The related work applies data-unaware or clustering heuristics to partition the study area, which results in incomplete enumeration of possible localities. In this study, we formally defined the LCPD problem where the candidate locality was defined using minimum orthogonal bounding rectangles (MOBRs). Then, we proposed a Quadruplet & Grid Filter-Refine (QGFR) algorithm that leveraged an MOBR enumeration lemma, and a novel upper bound on the participation index to efficiently prune the search space. The experimental evaluation showed that the QGFR algorithm reduced the computation cost substantially. One case study using the North American Atlas-Hydrography and U.S. Major City Datasets was conducted to discover local co-Location Patterns which would be missed if the entire dataset was analyzed or methods proposed by the related work were applied.
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a neighborhood graph based approach to regional co Location Pattern discovery a summary of results
Advances in Geographic Information Systems, 2011Co-Authors: Pradeep Mohan, Shashi Shekhar, James A Shine, James P Rogers, Zhe Jiang, Nicole M WayantAbstract:Regional co-Location Patterns (RCPs) represent collections of feature types frequently located together in certain localities. For example, RCP suggests that a co-Location Pattern involving alcohol-related crimes and bars is often localized to downtown regions. Given a set of Boolean feature types, their geo-located instances, a spatial neighbor relation, and a prevalence threshold, the RCP discovery problem finds all prevalent RCPs (pairs of co-Locations and their prevalence localities). RCP discovery is important in many societal applications, including public safety, public health, climate science and ecology. The RCP discovery problem involves three major challenges: (a) an exponential number of subsets of feature types, (b) an exponential number of candidate localities and (c) a tradeoff between accurately modeling Pattern locality and achieving computational efficiency. Related work does not provide computationally efficient methods to discover all interesting RCPs with their natural prevalence localities. To address these limitations, this paper proposes a neighborhood graph based approach that discovers all interesting RCPs and is aware of a Pattern's prevalence localities. We identify partitions based on the Pattern instances and neighbor graph. We introduce two new interest measures, a regional participation ratio and a regional participation index to quantify the strength of RCPs. We present two new algorithms, Pattern Space (PS) enumeration and Maximal Locality (ML) enumeration and show that they are correct and complete. Experiments using real crime datasets show that ML pruning outperforms PS enumeration.
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ICDM - Zonal Co-Location Pattern Discovery with Dynamic Parameters
Seventh IEEE International Conference on Data Mining (ICDM 2007), 2007Co-Authors: Mete Celik, James M. Kang, Shashi ShekharAbstract:Zonal co-Location Patterns represent subsets of feature- types that are frequently located in a subset of space (i.e., zone). Discovering zonal spatial co-Location Patterns is an important problem with many applications in areas such as ecology, public health, and homeland defense. However, discovering these Patterns with dynamic parameters (i.e., repeated specification of zone and interest measure values according to user preferences) is computationally complex due to the repetitive mining process. Also, the set of candidate Patterns is exponential in the number of feature types, and spatial datasets are huge. Previous studies have focused on discovering global spatial co-Location Patterns with a fixed interest measure threshold. In this paper, we propose an indexing structure for co-Location Patterns and propose algorithms (Zoloc-Miner) to discover zonal co- Location Patterns efficiently for dynamic parameters. Extensive experimental evaluation shows our proposed approaches are scalable, efficient, and outperform naive alternatives.
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Zonal Co-Location Pattern Discovery with Dynamic Parameters
Seventh IEEE International Conference on Data Mining (ICDM 2007), 2007Co-Authors: Mete Celik, James M. Kang, Shashi ShekharAbstract:Zonal co-Location Patterns represent subsets of feature- types that are frequently located in a subset of space (i.e., zone). Discovering zonal spatial co-Location Patterns is an important problem with many applications in areas such as ecology, public health, and homeland defense. However, discovering these Patterns with dynamic parameters (i.e., repeated specification of zone and interest measure values according to user preferences) is computationally complex due to the repetitive mining process. Also, the set of candidate Patterns is exponential in the number of feature types, and spatial datasets are huge. Previous studies have focused on discovering global spatial co-Location Patterns with a fixed interest measure threshold. In this paper, we propose an indexing structure for co-Location Patterns and propose algorithms (Zoloc-Miner) to discover zonal co- Location Patterns efficiently for dynamic parameters. Extensive experimental evaluation shows our proposed approaches are scalable, efficient, and outperform naive alternatives.
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ICDM - A join-less approach for co-Location Pattern mining: a summary of results
Fifth IEEE International Conference on Data Mining (ICDM'05), 2005Co-Authors: Shashi Shekhar, Mete CelikAbstract: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 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 cost of identifying the instances. The join-less co-Location mining algorithm is efficient since it uses an instance-lookup scheme instead of an expensive spatial or instance join operation for identifying co-Location instances. The experimental evaluations show the join-less algorithm performs more efficiently than a current join-based algorithm and is scalable in dense spatial datasets.
Lihua Zhou - One of the best experts on this subject based on the ideXlab platform.
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ICDE - Redundancy Reduction for Prevalent Co-Location Patterns
2018 IEEE 34th International Conference on Data Engineering (ICDE), 2018Co-Authors: Lizhen Wang, Lihua ZhouAbstract:Spatial co-Location Pattern mining is an interesting and important task in spatial data mining which discovers the subsets of spatial features frequently observed together in nearby geographic space. However, the traditional framework of mining prevalent co-Location Patterns produces numerous redundant co-Location Patterns, which makes it hard for users to understand or apply. In this paper we study the problem of reducing redundancy in a collection of prevalent co-Location Patterns. We first introduce the concept of semantic distance between two co-Location Patterns, and then define redundant co-Locations by introducing the concept of δ-covered, where δ (0≤δ≤1) is a coverage measure. We develop two algorithms RRclosed and RRnull to perform the redundancy reduction for prevalent co-Location Patterns. Our performance studies on the synthetic and real-world data sets demonstrate that our method effectively reduces the size of the original collection of closed co-Location Patterns by about 50%. Furthermore, the RRnull method runs much faster than the related closed co-Location Pattern mining algorithm.
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Redundancy Reduction for Prevalent Co-Location Patterns
2018 IEEE 34th International Conference on Data Engineering (ICDE), 2018Co-Authors: Lizhen Wang, Lihua ZhouAbstract:Spatial co-Location Pattern mining is an interesting and important task in spatial data mining which discovers the subsets of spatial features frequently observed together in nearby geographic space. However, the traditional framework of mining prevalent co-Location Patterns produces numerous redundant co-Location Patterns, which makes it hard for users to understand or apply. In this paper we study the problem of reducing redundancy in a collection of prevalent co-Location Patterns. We first introduce the concept of semantic distance between two co-Location Patterns, and then define redundant co-Locations by introducing the concept of δ-covered, where δ (0≤δ≤1) is a coverage measure. We develop two algorithms RRclosed and RRnull to perform the redundancy reduction for prevalent co-Location Patterns. Our performance studies on the synthetic and real-world data sets demonstrate that our method effectively reduces the size of the original collection of closed co-Location Patterns by about 50%. Furthermore, the RRnull method runs much faster than the related closed co-Location Pattern mining algorithm.
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Redundancy Reduction for Prevalent Co-Location Patterns
IEEE Transactions on Knowledge and Data Engineering, 2018Co-Authors: Lizhen Wang, Lihua ZhouAbstract:Spatial co-Location Pattern mining is an interesting and important task in spatial data mining which discovers the subsets of spatial features frequently observed together in nearby geographic space. However, the traditional framework of mining prevalent coLocation Patterns produces numerous redundant co-Location Patterns, which makes it hard for users to understand or apply. To address this issue, in this paper, we study the problem of reducing redundancy in a collection of prevalent co-Location Patterns by utilizing the spatial distribution information of co-Location instances. We first introduce the concept of semantic distance between a co-Location Pattern and its super-Patterns, and then define redundant co-Locations by introducing the concept of d-covered, where δ (0 ≤ δ ≤ 1) is a coverage measure. We develop two algorithms RRclosed and RRnull to perform the redundancy reduction for prevalent co-Location Patterns. The former adopts the post-mining framework that is commonly used by existing redundancy reduction techniques, while the latter employs the mine-and-reduce framework that pushes redundancy reduction into the co-Location mining process. Our performance studies on the synthetic and real-world data sets demonstrate that our method effectively reduces the size of the original collection of closed co-Location Patterns by about 50 percent. Furthermore, the RRnull method runs much faster than the related closed co-Location Pattern mining algorithm.
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Redundancy Reduction for Prevalent Co-Location Patterns
IEEE Transactions on Knowledge and Data Engineering, 2018Co-Authors: Lizhen Wang, Lihua ZhouAbstract:Spatial co-Location Pattern mining is an interesting and important task in spatial data mining which discovers the subsets of spatial features frequently observed together in nearby geographic space. However, the traditional framework of mining prevalent co-Location Patterns produces numerous redundant co-Location Patterns, which makes it hard for users to understand or apply. To address this issue, in this paper, we study the problem of reducing redundancy in a collection of prevalent co-Location Patterns by utilizing the spatial distribution information of co-Location instances. We first introduce the concept of semantic distance between a co-Location Pattern and its super-Patterns, and then define redundant co-Locations by introducing the concept of δ-covered , where $\delta \,(0\leq \delta \leq 1)$ is a coverage measure. We develop two algorithms RRclosed and RRnull to perform the redundancy reduction for prevalent co-Location Patterns. The former adopts the post-mining framework that is commonly used by existing redundancy reduction techniques, while the latter employs the mine-and-reduce framework that pushes redundancy reduction into the co-Location mining process. Our performance studies on the synthetic and real-world data sets demonstrate that our method effectively reduces the size of the original collection of closed co-Location Patterns by about 50 percent. Furthermore, the RRnull method runs much faster than the related closed co-Location Pattern mining algorithm.
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WISE (1) - Mining Co-Location Patterns with Dominant Features
Lecture Notes in Computer Science, 2017Co-Authors: Yuan Fang, Lizhen Wang, Xiaoxuan Wang, Lihua ZhouAbstract:The spatial co-Location Pattern mining discovers the subsets of spatial features which are located together frequently in geography. Most of the studies in this field use prevalence to measure a co-Location Pattern’s popularity, namely the frequencies of a spatial feature set participating in a spatial database. However, in some cases, users are not only interested in identifying the prevalence of a feature set, but also the features playing the dominant role in a Pattern. In this paper, we focus on mining dominant-feature co-Location Pattern (DFCP). We firstly propose a new measure, namely disparity, to measure the disparity of features in a Pattern. Secondly, we formulate the DFCP mining problem to determine DFCP and extract dominant features. Thirdly, an efficient algorithm is proposed for mining DFCP. Finally, we offer an experimental evaluation of the proposed algorithms on both real data sets and synthetic data sets in terms of efficiency, mining results and significance. The results show that our method can effectively discover DFCPs.
Mete Celik - One of the best experts on this subject based on the ideXlab platform.
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ICDM - Zonal Co-Location Pattern Discovery with Dynamic Parameters
Seventh IEEE International Conference on Data Mining (ICDM 2007), 2007Co-Authors: Mete Celik, James M. Kang, Shashi ShekharAbstract:Zonal co-Location Patterns represent subsets of feature- types that are frequently located in a subset of space (i.e., zone). Discovering zonal spatial co-Location Patterns is an important problem with many applications in areas such as ecology, public health, and homeland defense. However, discovering these Patterns with dynamic parameters (i.e., repeated specification of zone and interest measure values according to user preferences) is computationally complex due to the repetitive mining process. Also, the set of candidate Patterns is exponential in the number of feature types, and spatial datasets are huge. Previous studies have focused on discovering global spatial co-Location Patterns with a fixed interest measure threshold. In this paper, we propose an indexing structure for co-Location Patterns and propose algorithms (Zoloc-Miner) to discover zonal co- Location Patterns efficiently for dynamic parameters. Extensive experimental evaluation shows our proposed approaches are scalable, efficient, and outperform naive alternatives.
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Zonal Co-Location Pattern Discovery with Dynamic Parameters
Seventh IEEE International Conference on Data Mining (ICDM 2007), 2007Co-Authors: Mete Celik, James M. Kang, Shashi ShekharAbstract:Zonal co-Location Patterns represent subsets of feature- types that are frequently located in a subset of space (i.e., zone). Discovering zonal spatial co-Location Patterns is an important problem with many applications in areas such as ecology, public health, and homeland defense. However, discovering these Patterns with dynamic parameters (i.e., repeated specification of zone and interest measure values according to user preferences) is computationally complex due to the repetitive mining process. Also, the set of candidate Patterns is exponential in the number of feature types, and spatial datasets are huge. Previous studies have focused on discovering global spatial co-Location Patterns with a fixed interest measure threshold. In this paper, we propose an indexing structure for co-Location Patterns and propose algorithms (Zoloc-Miner) to discover zonal co- Location Patterns efficiently for dynamic parameters. Extensive experimental evaluation shows our proposed approaches are scalable, efficient, and outperform naive alternatives.
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ICDM - A join-less approach for co-Location Pattern mining: a summary of results
Fifth IEEE International Conference on Data Mining (ICDM'05), 2005Co-Authors: Shashi Shekhar, Mete CelikAbstract: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 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 cost of identifying the instances. The join-less co-Location mining algorithm is efficient since it uses an instance-lookup scheme instead of an expensive spatial or instance join operation for identifying co-Location instances. The experimental evaluations show the join-less algorithm performs more efficiently than a current join-based algorithm and is scalable in dense spatial datasets.
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A join-less approach for co-Location Pattern mining: a summary of results
Fifth IEEE International Conference on Data Mining (ICDM'05), 2005Co-Authors: Shashi Shekhar, Mete CelikAbstract: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 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 cost of identifying the instances. The join-less co-Location mining algorithm is efficient since it uses an instance-lookup scheme instead of an expensive spatial or instance join operation for identifying co-Location instances. The experimental evaluations show the join-less algorithm performs more efficiently than a current join-based algorithm and is scalable in dense spatial datasets.
Peizhong Yang - One of the best experts on this subject based on the ideXlab platform.
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A MapReduce approach for spatial co-Location Pattern mining via ordered-clique-growth
Distributed and Parallel Databases, 2019Co-Authors: Peizhong Yang, Lizhen Wang, Xiaoxuan WangAbstract:Spatial co-Location Pattern is a subset of spatial features whose instances are frequently located together in geography. Mining co-Location Patterns are particularly valuable for discovering spatial dependencies. Traditional co-Location Pattern mining algorithms are computationally expensive with rapidly increasing of data volume. In this paper, we explore a novel iterative framework based on parallel ordered-clique-growth for co-Location Pattern mining. The ordered clique extension can re-use previously processed information and be executed in parallel, and hence speed up the identification of co-Location instances. Based on the iterative framework, a MapReduce algorithm is designed to search for prevalent co-Location Patterns in a level-wise manner, namely PCPM_OC. To narrow the search space of ordered cliques, two pruning techniques are suggested for filtering invalid clique instances as much as possible. The completeness and correctness of PCPM_OC are proven and we also discuss its complexity in this paper. Moreover, we compare PCPM_OC with two advanced MapReduce based co-Location Pattern mining algorithms on multiple perspectives. At last, substantial experiments are conducted on synthetic and real-world spatial datasets to study the performance of PCPM_OC. Experimental results demonstrate that PCPM_OC has a significant improvement in efficiency and shows better scalability on massive spatial data.
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Mining high influence co-Location Patterns from instances with attributes
Evolutionary Intelligence, 2019Co-Authors: Dianwu Fang, Lizhen Wang, Peizhong Yang, Lan ChenAbstract:A spatial co-Location Pattern describes coexistence of spatial features whose instances frequently appear together in geographic space. Numerous studies have been proposed to discover interesting co-Location Patterns from spatial data sets, but most of them only use the Location information of instances. As a result, they cannot adequately reflect the influence between instances. In this paper, we take additional attributes of instances into account in the process of co-Location Pattern mining, and propose a new approach for discovering the high influence co-Location Patterns. In our approach, we consider the spatial neighboring relationships and the similarity of instances simultaneously, and utilize the information entropy approach to measure the influence of any instance exerting on its neighbors and the influence of any feature in a co-Location Pattern. Then, an influence index for measuring the interestingness of a co-Location Pattern is proposed and we prove the influence index measure satisfies the downward closure property that can be used for pruning the search space, and thus an efficient high influence co-Location Pattern mining algorithm is designed. At last, extensive experiments are conducted on synthetic and real spatial data sets. Experimental results reveal the effectiveness and efficiency of our method.
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An Effective Approach on Mining Co-Location Patterns from Spatial Databases with Rare Features
2019 20th IEEE International Conference on Mobile Data Management (MDM), 2019Co-Authors: Peizhong Yang, Lizhen Wang, Xiaoxuan Wang, Dianwu FangAbstract:A co-Location Pattern is a group of spatial features whose instances are frequently appearing together in geography. Co-Location Pattern mining is particularly valuable for discovering spatial dependencies. Lots of co-Location Pattern mining approaches have been proposed, but they often emphasize the equal participation of every spatial feature. As a result, the interesting Pattern which involves spatial features with significantly different for the number of instances cannot be captured. In this paper, we are committed to address the problem of mining co-Location Patterns from the spatial database with rare features. Specifically, we first propose a new interest measure, namely the weighted participation index. This interest measure is related to the distribution of the number of instances for spatial features, and it has ability to capture the prevalent co-Location Patterns with or without rare features. Furthermore, we prove that the weighted participation index possesses the approximate monotonicity property, which can be utilized to improve the computational efficiency, and thereby an efficient algorithm is developed. As demonstrated by extensive experiments, our approach is effective, efficient and scalable for mining co-Location Patterns embedded in the spatial database with rare features.
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MDM - An Effective Approach on Mining Co-Location Patterns from Spatial Databases with Rare Features
2019 20th IEEE International Conference on Mobile Data Management (MDM), 2019Co-Authors: Peizhong Yang, Lizhen Wang, Xiaoxuan Wang, Dianwu FangAbstract:A co-Location Pattern is a group of spatial features whose instances are frequently appearing together in geography. Co-Location Pattern mining is particularly valuable for discovering spatial dependencies. Lots of co-Location Pattern mining approaches have been proposed, but they often emphasize the equal participation of every spatial feature. As a result, the interesting Pattern which involves spatial features with significantly different for the number of instances cannot be captured. In this paper, we are committed to address the problem of mining co-Location Patterns from the spatial database with rare features. Specifically, we first propose a new interest measure, namely the weighted participation index. This interest measure is related to the distribution of the number of instances for spatial features, and it has ability to capture the prevalent co-Location Patterns with or without rare features. Furthermore, we prove that the weighted participation index possesses the approximate monotonicity property, which can be utilized to improve the computational efficiency, and thereby an efficient algorithm is developed. As demonstrated by extensive experiments, our approach is effective, efficient and scalable for mining co-Location Patterns embedded in the spatial database with rare features.
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DASFAA (1) - A Parallel Spatial Co-Location Pattern Mining Approach Based on Ordered Clique Growth
Database Systems for Advanced Applications, 2018Co-Authors: Peizhong Yang, Lizhen Wang, Xiaoxuan WangAbstract:Co-Location Patterns or subsets of spatial features, whose instances are frequently located together, are particularly valuable for discovering spatial dependencies. Although lots of spatial co-Location Pattern mining approaches have been proposed, the computational cost is still expensive. In this paper, we propose an iterative mining framework based on MapReduce to mine co-Location Patterns efficiently from massive spatial data. Our approach searches for co-Location Patterns in parallel through expanding ordered cliques and there is no candidate set generated. A large number of experimental results on synthetic and real-world datasets show that the proposed method is efficient and scalable for massive spatial data, and is faster than other parallel methods.
