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.

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.

High utility co-location patterns (HUCPs) are more meaningful than traditional prevalent co-location patterns (PCPs) since HUCPs consider the utility of instances that reflects the significance of the instances in reality. However, the mining result normally contains too many HUCPs, this makes HUCPs difficult for users to understand, absorb and apply. This demonstration presents a δ-closed high utility co-location pattern miner (δ-CHUCPM), a mining system that removes redundant HUCPs and presents a concise representation of the mining result to users. We achieve the removing redundant HUCP objective by allowing a tolerance between the utility of a pattern and the utility of its superset. If the tolerance is not larger than a user-specified small value δ, the pattern is removed. Users can freely set different δ tolerance values according to their own application scenarios and requirements to control the number of HUCPs. δ-CHUCPM can provide users with a more understandable result and help them make informed decisions.

A co-location pattern is a set of spatial features that are strongly correlated in space. However, some of these patterns could be neglected if the prevalence metrics are based solely on the clique (or star) relationship. Hence, the l-reachability co-location pattern is proposed by introducing the l-reachability clique where the members of each instance pair can be reachable to each other in a given step length l. Because the average size of l-reachability co-location patterns tends to be longer, maximal l-reachability co-location pattern mining is researched in this paper. First, some sparsification strategies are introduced to shorten star neighborhood lists of instances in an updated graph called the l-reachability neighbor relationship graph, and then, they are grouped by their corresponding patterns. Second, candidate maximal l-reachability co-location patterns are iteratively detected in a size-independent way on bi-graphs that contain group keys and their intersection sets. Third, the prevalence of each candidate maximal l-reachability co-location pattern is checked in a binary search way with a natural l-reachability clique called the ⌊l/2⌋-reachability neighborhood list. Finally, the effectiveness and efficiency of our model and algorithms are analyzed by extensive comparison experiments on synthetic and real-world spatial data sets.

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.