Mining regional co-location pattern (RCP) in urban areas can reveal local correlations among urban facilities, which can inform urban planning and development. However, existing mining methods lack considerations of user demand for temporal accessibility and user preferences for the regional structure. Therefore, the mined results may be impractical. To address these issues, we propose a novel RCP mining method with temporal accessibility and regional structure (TARS). First, we utilized travel time as a measure of temporal accessibility between instances and proposed the concept of an accessible path to develop a method to regionalize temporal accessibility. To enhance efficiency, we developed a neighbor search algorithm and two pruning strategies to accelerate regional division. Second, we propose a measure called spatial region preference (SRP) to help select the preferred RCPs. Extensive experiments conducted on synthetic and real datasets show that our proposed RCP mining algorithm can discover more practical patterns and run faster than the selected baseline algorithms.

Spatial co-location pattern mining aims to discover sets of spatial features that prevalently occur together. However, existing methods for local co-location pattern (LCP) mining often yield prevalent regions with limited spatial coverage, resulting in the overestimation of pattern interest measures and the omission of implicit spatial associations. Furthermore, the distinction between pattern categories is often obscured, as global co-location patterns can be regarded as special cases of LCPs. To address these limitations, we propose a novel framework that can identify prevalent regions with broader coverage and achieve clear pat tern classification. We first prove that the problem of maximizing each prevalent region is NP-hard. Then we formally define the concept of maximal prevalent regions as a viable alternative and develop a heuristic dynamic spatial expansion algorithm for their efficient identification. In addition, we introduce a spatial occu pancy ratio and a three-level classification scheme (rare, common, and significant) to replace the traditional global/local dichotomy. Finally, a Delaunay-triangulation-based method is employed to quantify the coverage of non-convex regions, ensuring accurate occupancy calculations. Extensive experiments on synthetic and real-world datasets demonstrate the effectiveness and efficiency of the proposed framework.

Abstract Discovering the correlation relationships of spatial facilities in urban functional regions is of great significance in analyzing urban planning and promoting urban development. However, existing regional co-location pattern mining methods do not fully consider the attributes of urban functional regions. Consequently, they fail to effectively capture the relationships between spatial facilities and regions, resulting in patterns lacking practical application value and interpretability. To address this issue, this paper proposes a novel regional co-location pattern (RCP) mining method based on urban functional region division. First, an ontology-based method for urban functional region division is proposed. On the basis of dividing urban functional regions, the expected mean distance is generated to adaptively calculate the neighbor relationships between instances in each region, and a parallel algorithm is designed to quickly mine RCPs in urban functional regions. Then, the correlation coefficient ( $$CC_{RP}$$ C C RP ) measures the association strength between RCP and function type to provide interpretability for RCPs. Finally, extensive experiments on real-world datasets are conducted to verify the effectiveness of urban functional region partition and to demonstrate the superiority of the proposed RCP mining method.

Groups of spatial features whose instances frequently appear together in nearby areas are regarded as prevalent co-location patterns (PCPs). Traditional PCP mining ignores the significance of instances and features. However, in reality, these instances and features have different significance, the traditional PCPs may not sufficiently expose knowledge from spatial data. This study focuses on discovering high utility co-location patterns (HUCPs) in which each instance is assigned a utility to reflect its significance. To filter HUCPs, an adaptive utility participation index (UPI) is designed. Unfortunately, the UPI does not hold the downward closure property. The performance of mining HUCPs is very inefficient since unnecessary candidates cannot be early pruned. Thus, an efficient clique-querying mining framework is devised without generating candidates. This framework first divides neighboring instances into cliques, then compacts these cliques into a hash table structure. Next, the adaptive UPI of any patterns can be quickly calculated based on their participating instances that are obtained by executing a querying scheme on the hash table. Finally, HUCPs are filtered efficiently. The effectiveness and efficiency of the proposed method are proved in both theory and experiments to make a promise that the patterns mined are more meaningful and the mining performance is significantly improved compared to the previous methods.

The spatial co-location pattern is a collection of spatial features in which instances of features prevalently appear in neighboring spatial regions. Due to the heterogeneity of spatial data distribution, the instances of some patterns appear prevalently in the global region (i.e., Global Prevalent Co-location Patterns, GPCPs), while some patterns are not prevalent globally, and their instances are clustered only in some local regions (i.e., Local Prevalent Co-location Patterns, LPCPs). Multi-level co-location pattern mining aims to mine these two types of patterns simultaneously, but existing methods cannot accurately judge the spatial distribution of patterns in a certain region, leading to unsuitable judgment of both GPCPs and LPCPs. To overcome this problem, this paper firstly proposes the relative distribution coefficient to identify the spatial distribution form of patterns, and provides a more refined way for discovering both GPCPs and LPCPs. Secondly, a novel multi-level co-location pattern mining algorithm is proposed by using the relative distribution coefficient as the interest metrics, and some pruning strategies are suggested to improve the mining efficiency. Finally, extensive experiments are conducted on both real and synthetic datasets to verify the effectiveness and efficiency of the proposed method.

Community search (CS) is an important research topic in network analysis, which aims to find a subgraph that satisfies the given conditions. A dynamic attribute heterogeneous information network (DAHIN) is a sequence of attribute heterogeneous information network (AHIN) snapshots, where each snapshot consists of multiple types of vertices as well as edges, and each vertex is associated a set of attribute keywords. CS over DAHIN faces many challenges. In this paper, we study the CS problem over DAHINs, aiming to search for cohesive subgraphs containing query vertex and simultaneously satisfying the connectivity, attribute cohesiveness and interaction stability. To this end, we propose a deep learning model with a three-level attention mechanism and the concept of interaction frequency with respect to multiple semantic relationships to measure the similarity of attributes and the stability of interactions between vertices respectively. In addition, we design three search algorithms to locate the target community by optimizing the degree of interaction stability and attribute similarity between vertices. Extensive experiments, including comparison with existing algorithms, ablation analysis, parameter sensitivity examination, and case studies, are conducted on four real-world datasets to validate the effectiveness and efficiency of the proposed model and search algorithms. The code and model of CS-DAHIN will be open source on GitHub.

Co-location is one of the most fundamental spatial associations in spatial data. Previous anomalous co-location detections only aim to detect abnormal inter-feature co-locations on bivariate datasets, while the abnormal intra-feature co-locations are lost, which may have limits to applications. Thus, we propose a novel multi-view anomalous co-location detection framework (MACDF). Two kinds of relationships are captured by two proposed couplings, that is, intra-feature coupling and inter-feature coupling (intra-coupling and inter-coupling for short). Moreover, the neighboring concept in co-location relationships is redefined with a mixture-considered inter-coupling for point-like instances, inspired by the concept for polygonal instances, and a concreted algorithm, i.e. multi-view low-rank analysis with direction for anomalous co-location detection (MLAD-ACD), is designed to discover the anomalous co-locations, on not only bivariate datasets but also multivariate datasets. Experiments show the instantiated algorithm MLAD-ACD can detect anomalous co-locations effectively and efficiently on spatial datasets. Specifically, MLAD-ACD obtains at least a 71% higher AUC score than the state-of-the-art algorithms in an acceptable time.

Prevalent co-location pattern mining aims to find a subset of spatial features whose instances are frequently located together in geo-space. Sub-prevalent co-location pattern mining attains co-location patterns with richer spatial relationships based on star instance model instead of clique instance model. Further, discovering dominant features in a sub-prevalent co-location pattern is important to reveal the interaction relationship between spatial features and improve the application of the pattern. However, the methods for mining sub-prevalent co-location pattern with dominant feature use a distance threshold to determine the binary neighbor relationship between spatial instances, and ignore the fuzziness of neighbor relationship which can give a rise to the fuzziness of dominance relationship between spatial features. Thus, this paper presents mining fuzzy sub-prevalent co-location pattern with dominant feature by exploring spatial instance distribution based on the fuzzy theory. Specifically, a novel pattern, fuzzy sub-prevalent co-location pattern with dominant feature, is proposed by defining the fuzzy neighbor relationship between spatial instances and the fuzzy dominant relationship between spatial features. Then, an efficient algorithm to mine the proposed patterns is designed by utilizing the anti-monotonicity of fuzzy star participation index and fuzzy star dominance index to prune unpromising patterns. The experimental results show that the proposed patterns are practical and the mining algorithm is efficient.

Spatial co-location pattern mining (SCPM) aims to discover subsets of spatial features frequently located in nearby geographic space. Previous studies of SCPM only concern the inter-features association of a pattern, but neglect the interesting intra-feature behavior. In this paper, we propose spatial co-location pattern mining with coupling relation consideration (SCPM-CR) to capture complex relations in a co-location. Specifically, InterPCI is proposed to capture the inter-features coupling in a pattern, and IntraCAI is designed to capture the congregating behavior of intra-feature objects. Based on the anti-monotone property of InterPCI, a general framework which searches for patterns in a level-wise manner is suggested. To tackle the participating object search problem, a candidate-and-search algorithm with a heuristic backtracking search is proposed, namely CS-HBS. Extensive experiments are conducted to demonstrate the superiority of SCPM-CR, and also to validate the efficiency and scalability of CS-HBS.

Spatial co-location pattern mining (SCPM) aims to discover subsets of spatial features frequently located together in proximate areas. Previous studies of SCPM solely concern the inter-features association of a pattern, but neglect the interesting intra-feature behavior. In this paper, we propose the task of spatial co-location pattern mining with coupling relation consideration (SCPM-CR) to capture complex relations embedded in a co-location. Specifically, InterPCI measure is designed to capture the inter-features coupling by considering the comprehensive interaction of objects for the features in a pattern, and luckily it possesses the anti-monotone property. Another measure, IntraCAI, is proposed to capture the congregating behavior of intra-feature objects under the restriction of a co-location. A general framework is designed for SCPM-CR task and experiments show that a large fraction of computation time is devoted to identifying the participating objects of a candidate pattern. To tackle this calculation bottleneck, a novel candidate-and-search algorithm is suggested, CS-HBS, equipped with heuristic backtracking search. Extensive experiments are conducted on real and synthetic datasets to demonstrate the superiority of SCPM-CR compared with traditional SCPM methods, and also to validate the efficiency and scalability of CS-HBS. Experimental results show that CS-HBS outperforms the baselines by several times or even orders of magnitude.

A co-location pattern is a subset of spatial features whose instances are frequently located together in proximate areas. Mining co-location patterns can discover spatial dependencies in spatial datasets and have particular value in many applications. However, it is challengeable to discover co-location patterns from massive spatial datasets, due to the expensive computational cost. In this paper, we present a novel parallel co-location pattern mining approach. First, dividing spatial neighbor relationships into some neighbor-dependency partitions enables to perform mining task on each partition independently in parallel. Then, a column-based calculation approach is proposed to replace the time-consuming generation of table instances for calculating the prevalence of patterns. To further reduce the search space of patterns on each partition, two pruning strategies are suggested. We implement the parallel co-location pattern mining algorithm based on neighbor-dependency partition and column calculation via MapReduce, named PCPM-NDPCC. Substantial experiments are conducted on real and synthetic datasets to examine the performance of PCPM-NDPCC. Experimental results reveal that PCPM-NDPCC has a significant improvement in efficiency than baseline algorithms and shows better scalability for massive spatial data processing.

Abstract Multi-view clustering (MVC), which aims to explore the underlying structure of data by leveraging heterogeneous information of different views, has brought along a growth of attention. Multi-view clustering algorithms based on different theories have been proposed and extended in various applications. However, most existing MVC algorithms are shallow models, which learn structure information of multi-view data by mapping multi-view data to low-dimensional representation space directly, ignoring the nonlinear structure information hidden in each view, and thus, the performance of multi-view clustering is weakened to a certain extent. In this paper, we propose a deep multi-view clustering algorithm based on multiple auto-encoder, termed MVC-MAE, to cluster multi-view data. MVC-MAE adopts auto-encoder to capture the nonlinear structure information of each view in a layer-wise manner and incorporate the local invariance within each view and consistent as well as complementary information between any two views together. Besides, we integrate the representation learning and clustering into a unified framework, such that two tasks can be jointly optimized. Extensive experiments on six real-world datasets demonstrate the promising performance of our algorithm compared with 15 baseline algorithms in terms of two evaluation metrics.

Network embedding (NE) is a method that maps nodes in a network into a low-dimensional and continuous vector space while maintains inherent features of the network. Most existing algorithms for NE focus on one or two of the aspects of topological structure, node attributes or community structure information, but without integrating the three in a unified framework. In this study, we develop a deep neural network-based framework for Attributed Network Embedding with Community Preservation (ANECP), which simultaneously incorporates the topological structure, node attributes as well as community structure together to obtain the low-dimensional distributed representations of nodes in the network. The use of deep neural networks captures the underlying high non-linearity in both topology and attribute information, while the incorporation of the community structure resolves the issues of data sparsity from microscopic perspective. Consequently, the obtained node representations can preserve proximity and discriminative. We conducted experimental studies using six real-world datasets. The experimental results show that proposed ANECP has superior performance over the existing methods.

A huge volume of spatio-textual objects generated from location-based services enable a wide range of spatial keyword queries. Recently, researchers have proposed a novel query, called Spatial Pattern Matching (SPM), which uses a pattern to capture users' intention. It has been demonstrated to be useful but computationally intractable. Existing algorithms suffer from the low efficiency issue, especially on large scale datasets. To enhance the performance of SPM, in this paper we propose a novel Efficient Spatial Pattern Matching (ESPM) algorithm, which exploits the inverted linear quadtree index and computes matched node pairs and object pairs level by level in a top-down manner. In particular, it focuses on pruning unpromising nodes and node pairs at the high levels, resulting in a large number of unpromising objects and object pairs to be pruned before accessing them from disk. Our experimental results on real large datasets show that ESPM is over one order of magnitude faster than the state-of-the-art algorithm, and also uses much less I/O cost.

With recent advances in information technologies such as global position system and mobile internet, a huge volume of spatio-textual objects have been generated from location-based services, which enable a wide range of spatial keyword queries. Recently, researchers have proposed a novel query, called Spatial Pattern Matching (SPM), which uses a pattern to capture the user's intention. It has been demonstrated to be fundamental and useful for many real applications. Despite its usefulness, the SPM problem is computationally intractable. Existing algorithms suffer from the low efficiency issue, especially on large scale datasets. To enhance the performance of SPM, in this paper we propose a novel Efficient Spatial Pattern Matching (ESPM) algorithm, which exploits the inverted linear quadtree index and computes matched node pairs and object pairs level by level in a top-down manner. In particular, it focuses on pruning unpromising nodes and node pairs at the high levels, resulting in a large number of unpromising objects and object pairs to be pruned before accessing them from disk. We experimentally evaluate the performance of ESPM on real large datasets. Our results show that ESPM is over one order of magnitude faster than the state-of-the-art algorithm, and also uses much less I/O cost.

Point of Interest (POI) is the core element of check-in data. It is an effective way to represent POI by distributed representation which can encode the information of POI into a continuous vector space. In this work, we present a hybrid model that map the concepts of network representation learning (NRL) to learn the position of POI and map the concepts of nature language processing (NLP) to learn the category of POI. The results, which are in vector form, can be widely applied to various location based services (LBS) without complicated artificial feature extraction. Further, it can also improve the performance of LBS. By a range of experiments on real datasets, we demonstrate our model's capability at characterizing POI. We also verify the effectiveness of the hybrid model on POI recommendation.

A co-location pattern refers to the subset of features which frequently appear together in spatial proximity. There are many literatures studied the approach of discovering co-location patterns. However, a lot of proposed approaches need some thresholds given by the user, and it is difficult to give the proper thresholds. Moreover, most proposed approaches treat the spatial object as a point during the mining process, but spatial objects are dynamic or appear in the form of a cluster normally, which means that their locations are polygons rather than points. This paper provides a novel framework to mine co-location patterns from spatial regional objects. At first, we redefine the interest measure of significant co-locations. In our framework, the user does not need to specify any threshold, and the redefined interest measure is monotonically non-increasing which can be used for improving the mining efficiency. Then, an algorithm based on the grid partition is proposed to reduce time complexity further. Finally, we verify the efficiency and effectiveness of the proposed approach by extensive experiments.

Spatial co-location patterns are groups of spatial features whose instances are frequently located together in spatial proximity. Most existing algorithms of discovering spatial co-location patterns are based on the candidate-test model, which is computationally expensive. When the user adjusts the participation index (PI) threshold, these algorithms have to be re-executed from the size 2 co-location patterns. In this paper, we propose a novel spatial instance partition method for mining co-location patterns which called overlap maximal clique partitioning algorithm (OMCP). The OMCP co-location mining algorithm divides instances of an input spatial dataset into a set of overlap maximal cliques. Table instances of all colocation patterns are collected by the overlap maximal cliques. Prevalent co-location patterns are directly calculated without generating the candidate patterns. The OMCP algorithm only needs to execute once to get the PI of all patterns, without re-executing when the PI threshold is adjusted. Our algorithm is performed on both synthetic and real-world datasets to demonstrate that the OMCP algorithm improvements in efficiency of co-location pattern mining.

Spatial co-location pattern mining is an important branch in the spatial data mining area, which discovers subsets of spatial features whose instances are frequently located together in the geographic space. The proximity between instances is defined by a distance threshold given by the user in traditional spatial co-location pattern mining. However, the user doesn't know which distance threshold is appropriate in most cases, even for experts. Besides, different densities of instance distribution are not considered in a dataset when using a unified distance threshold to measure the proximity. Also, global topological relations of instances are ignored in mining. In this paper, we consider the global topological relations by constructing Delaunay triangulation of spatial instances and calculate a distance constraint for each instance based on the constructed Delaunay triangulation. We redefine the proximity of instances according to the distance constraint so that users don't have to worry about giving an appropriate distance threshold when mining prevalent co-location patterns. We propose a new algorithm PTB based on a proximity relationship tree P-tree which stores the proximity relationships between instances. The experimental evaluation of several real-world datasets shows that our algorithm can get better results. We also evaluate each parameter and the number of features and instances affecting the efficiency of the algorithm by using synthetic datasets.

Spatial co-location mining is a key problem in urban planning and marketing. Current spatial co-location mining methods ignore the people who are related to the co-location patterns' instances, which results that the mining results are hard to explain and understand by the users. In this paper, we combine the theories of co-location mining and social networks analysis to mine a kind of special co-location patterns: Co-visiting patterns, which consider spatial information and social information at the same time. A co-visiting pattern is also a spatial feature set, whose instances are always visited by the similar users and located in a nearby region. We propose some new measures, including the user similarity, the weight of neighborhood relationship of two visited spatial instances, and the prevalent degree of a co-visiting pattern. In addition, we also explore the properties of the co-visiting patterns in this paper, and present an efficient algorithm. Finally, experiments and a detailed analysis are given at the end of this paper. Experimental results show that the rationality of co-visiting pattern, and the effectiveness and stability of the mining algorithm.

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.

Spatial co-location pattern mining is an important task in spatial data mining. However, traditional mining frameworks often produce too many prevalent patterns of which only a small proportion may be truly interesting to end users. To satisfy user preferences, this work proposes an interactive probabilistic post-mining method to discover user-preferred co-location patterns from the early-round of mined results by iteratively involving user's feedback and probabilistically refining preferred patterns. We first introduce a framework of interactively post-mining preferred co-location patterns, which enables a user to effectively discover the co-location patterns tailored to his/her specific preference. A probabilistic model is further introduced to measure the user feedback-based subjective preferences on resultant co-location patterns. This measure is used to not only select sample co-location patterns in the iterative user feedback process but also rank the results. The experimental results on real and synthetic data sets demonstrate the effectiveness of our approach.

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.

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.

A spatial colocation pattern is a group of spatial features whose instances are frequently located together in geographic space. Discovering colocations has many useful applications. For example, colocated plant species discovered from plant distribution data sets can contribute to the analysis of plant geography, phytosociology studies, and plant protection recommendations. In this paper, we study the colocation mining problem in the context of uncertain data, as the data generated from a wide range of data sources are inherently uncertain. One straightforward method to mine the prevalent colocations in a spatially uncertain data set is to simply compute the expected participation index of a candidate and decide if it exceeds a minimum prevalence threshold. Although this definition has been widely adopted, it misses important information about the confidence which can be associated with the participation index of a colocation. We propose another definition, probabilistic prevalent colocations, trying to find all the colocations that are likely to be prevalent in a randomly generated possible world. Finding probabilistic prevalent colocations (PPCs) turn out to be difficult. First, we propose pruning strategies for candidates to reduce the amount of computation of the probabilistic participation index values. Next, we design an improved dynamic programming algorithm for identifying candidates. This algorithm is suitable for parallel computation, and approximate computation. Finally, the effectiveness and efficiency of the methods proposed as well as the pruning strategies and the optimization techniques are verified by extensive experiments with “real + synthetic” spatially uncertain data sets.