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Malignant tumors are one of the serious diseases that endanger human health. The use of data mining techniques to mine the relationship between malignant tumors and various pathogenic factors has been attracted more and more attention. In practice, the relationship between tumor diseases and pathogenic factors is often fuzzy, and the occurrence of tumor diseases is not only affected by a single factor. However, there is no research to address the above problem. For this reason, the concept of fuzzy co-location patterns is proposed that combines the spatial co-location pattern mining with fuzzy theory, and pollution sources are fuzzified by the clustering method. Then, the method of extracting rules from the decision table is adopted to extract rules, and the corresponding confidence calculation algorithm is designed. As a result, a novel method which can discover the fuzzy relationships between tumor diseases and pathogenic factors is proposed. The effectiveness of the proposed method is verified on the actual case, and the effects of different parameters on the running time of the algorithm are analyzed by conducting experiments on synthetic datasets. At last, the theoretical analysis of the time complexity of the proposed algorithm is presented.

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. [ABSTRACT FROM AUTHOR]

A spatial prevalent co-location pattern (SPCP) is a subset of spatial features whose instances frequently appear together in geographic space. Memory-based neighbor relationship materialization method to search for pattern's instances is efficient, but instance information is stored repeatedly. Graph database technology can efficiently model data with complex associations. Thus, it is possible to consider using the graph database technology to materialize neighbor relationships (i.e., to construct neighborhood graph), but directly transplanting existing mining methods cannot exert the advantages of graph traversal. To solve the above problem, this paper explores the graph databasebased approach to mine spatial prevalent co-location patterns. Firstly, the graph database is utilized to model the spatial instances and their neighbor relations, i.e., the instances and relations are stored in the graph database to construct the neighborhood graph. Then, a basic algorithm called subgraph (or clique) search is designed based on the graph database, using the way of clique search strategy to generate a pattern's table instance to obtain the participating instances, and avoid the inefficient combination or join operations in the traditional method. Considering the low efficiency of collecting participating instances by generating table instances, a participating instance verification algorithm is designed, including the filtering and verification phases. The filtering phase determines whether the features involved in the center instance's neighborhoods fully contain the features in the pattern, and the verification phase determines whether there is pattern instance containing the central instance. The participating instance verification algorithm determines as many participating instances as possible each time, thereby effectively reducing the search space and the number of clique searches. In addition, the correctness and completeness of the proposed algorithms are proven. Finally, extensive experiments are conducted on real and synthetic datasets to verify the efficiency and effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

A co-location pattern indicates a group of spatial features whose instances are frequently located together in proximate geographic area. Spatial co-location pattern mining (SCPM) is valuable for many practical applications. Numerous previous SCPM studies emphasize the equal participation per feature. As a result, the interesting co-locations with rare features cannot be captured. In this paper, we propose a novel interest measure, i.e., the weighted participation index (WPI), to identify co-locations with or without rare features. The WPI measure possesses a conditional anti-monotone property which can be utilized to prune the search space. In addition, a fast row instance identification mechanism based on the ordered NR-tree is proposed to enhance efficiency. Subsequently, the ordered NR-tree-based algorithm is developed. To further improve efficiency and process massive spatial data, we break the ordered NR-tree into multiple independent subtrees, and parallelize the ordered NR-tree-based algorithm on MapReduce framework. Extensive experiments are conducted on both real and synthetic datasets to verify the effectiveness, efficiency and scalability of our techniques. [ABSTRACT FROM AUTHOR]

Spatial prevalent co-location pattern mining is to discover interesting and potentially useful patterns from spatial data, and it plays an important role in identifying spatially correlated features in many domains, such as Earth science and Public transportation. Existing approaches in this field only take into account the clique instances where feature instances form a clique. However, they may neglect some important spatial correlations among features in practice. In this paper, we introduce star participation instances to measure the prevalence of co-location patterns such that spatially correlated instances which cannot form cliques will also be properly considered. Then we propose a new concept called sub-prevalent co-location patterns (SPCP) based on the star participation instances. Furthermore, two efficient algorithms -- the prefix-tree-based algorithm (PTBA) and the partition-based algorithm (PBA) -- are proposed to mine all the maximal sub-prevalent co-location patterns (MSPCP) in a spatial data set. PTBA uses a typical candidate generate-and-test way starting from candidates with the longest pattern-size, while PBA adopts a step-by-step manner starting from 3-size core patterns. We demonstrate the significance of our proposed new concepts as well as the efficiency of our algorithms through extensive experiments. [ABSTRACT FROM AUTHOR]

• A new cell-based method for mining co-locations on extended objects was presented. • Cell-relation operations replaced instance computing to speed up the calculation. • Neighbor relations were materialized into feature transactions of cells. • The algorithm was compared with the latest algorithm and the classical one. Spatial co-location pattern mining (SCPM) is intended to discover subsets of spatial features whose instances are frequently located together in geographic areas. Traditional SCPM methods are designed for point spatial instances. However, in reality, instances are mostly in the form of extended objects, e.g., lines, polygons. In addition, current SCPM methods with extended objects are less well researched and have two disadvantages: (1) Existing researches cannot effectively capture neighborhood relationships between extended objects and their mining results cannot properly reflect the distribution dependence of features; (2) These methods are not efficient enough with large datasets. This paper proposes a novel framework called cell-relation operations framework to overcome these issues. To eliminate the first shortcoming, the framework uses the area overlapping of buffers between objects to gain the neighbor relationships between extended objects and introduces participation index under buffer size k to identify prevalent co-location patterns. To address the second problem, our framework employs cell-relation operations rather than instance relation computing as the basic computing unit for co-location mining, which substantially speeds up the computation. The framework obtains spatial co-locations by counting the feature transactions of the cells and calculates the feature overlap ratio of the cells to generate co-locations. We implement experiments with real datasets to demonstrate that our framework's mining results are more reasonable and the proposed framework's runtime outperforms the baselines by 2 to 4 orders of magnitude. [ABSTRACT FROM AUTHOR]