Although the probable and fuzzy viewshed have been recognized as critical in visibility analysis, they remain underutilized in practical applications such as surveillance drone positioning, telecommunications tower placement, and helicopter battle-position selection. Traditional approaches often assume a binary (boolean) notion of visibility, overlooking real-world factors like uncertainty in terrain data, partial occlusion from vegetation, or the effect on visibility by light sources, atmospheric haze, and target size. This scoping review systematically maps research on non-boolean visibility models and identifies several key gaps. First, there is a lack of methods that integrate both probabilistic and fuzzy approaches for observer placement. Second, while research has addressed DEM uncertainty and vegetation, few studies combine multiple factors or apply their methods to multi-observer or path-planning problems. Finally, research into 3D applications remains sparse, even though such work is critical for tasks like military helicopter missions or surveillance drone flights. Consequently, we highlight the need for more robust modeling of combined visibility factors and clearer strategies for incorporating both probable and fuzzy criteria in real-world operational settings. Bridging these gaps will enable more accurate and reliable visibility analyses across diverse domains, from city planning to helicopter mission planning.

In the rapidly evolving field of news recommendation, where user preferences are highly dynamic and content quickly becomes obsolete, providing timely and relevant recommendations presents a significant challenge. Traditional recommender systems typically rely on complex collaborative filtering models that depend on extensive user histories. In the news domain, however, such histories are often scarce due to the high prevalence of anonymous users. To address these challenges, we introduce a novel session-based recommendation method that leverages cohesive sequential pattern mining. Rather than relying on traditional frequency-based pattern utility metrics, our approach prioritizes pattern cohesiveness, which captures the temporal proximity of item interactions within a pattern, resulting in recommendations that align more closely with the user’s ongoing session.We conduct a comprehensive empirical evaluation of our approach using four large-scale real-world news datasets. The results demonstrate that our method, SeQcsp, significantly outperforms state-of-the-art session-based recommendation algorithms in terms of accuracy, ranking quality, as well as diversity. Furthermore, SeQcsp provides recommendations faster than most existing methods and is effective for both short and long user sessions, highlighting its robustness, adaptability, and efficiency.

The discovery of repeated structures in time series, known as motifs, is an important data mining task. Various techniques exist to mine motifs in either a database of time series or within one or two individual time series, either for a user-defined motif length or a range of lengths. However, mining frequent motifs of variable length in large time series databases remains an unsolved task that is computationally expensive. We propose FRM-Miner, an efficient algorithm for discovering more informative patterns in time series data, i.e., motifs of different length that are non-overlapping, occur frequently and where the euclidean distance between the motif and its various occurrences is minimal. Unlike current state-of-the-art approaches, FRM-Miner can efficiently find variable motif lengths in large time series databases. Through extensive experimentation, we show desirable properties of FRM-Miner, such as robustness to noise and expressive power, thereby discovering motifs that remain undetected using state-of-the-art methods. Additionally, our method is highly scalable, taking only 2.95 hours to discover informative sets of motifs on all 128 time series data sets of the UCR Time Series Archive, where related state-of-the-art algorithms such as Ostinato require several days.

Finding patterns in long event sequences is an important data mining task. Two decades ago research focused on finding all frequent patterns, where the anti-monotonic property of support was used to design efficient algorithms. Recent research focuses on producing a smaller output containing only the most interesting patterns. To achieve this goal, we introduce a new interestingness measure by computing the proportion of the occurrences of a pattern that are cohesive. This measure is robust to outliers, and is applicable to sequential patterns. We implement an efficient algorithm based on constrained prefix-projected pattern growth and pruning based on an upper bound to uncover the set of top-k quantile-based cohesive sequential patterns. We run experiments to compare our method with existing state-of-the-art methods for sequential pattern mining and show that our algorithm is efficient and produces qualitatively interesting patterns on large event sequences.

Multi-label classification problems occur naturally in different domains. For example, within text categorization the goal is to predict a set of topics for a document, and within image scene classification the goal is to assign labels to different objects in an image. In this work we propose a combination of two variations of k nearest neighborhoods (kNN) where the first neighborhood is computed instance (or row) based and the second neighborhood is feature (or column) based. Instance based kNN is inspired by user-based collaborative filtering, while feature kNN is inspired by item-based collaborative filtering. Finally we apply a linear combination of instance and feature neighbors scores and apply a single threshold to predict the set of labels. Experiments on various multi-label datasets show that our algorithm outperforms other state-of-the-art methods such as ML-kNN, IBLR and Binary Relevance with SVM, on different evaluation metrics. Finally our algorithm uses an inverted index during neighborhood search and scales to extreme datasets that have millions of instances, features and labels.

Sequence classification is an important task in data mining. We address the problem of sequence classification using rules composed of interesting patterns found in a dataset of labelled sequences and accompanying class labels. We measure the interestingness of a pattern in a given class of sequences by combining the cohesion and the support of the pattern. We use the discovered patterns to generate confident classification rules, and present two different ways of building a classifier. The first classifier is based on an improved version of the existing method of classification based on association rules, while the second ranks the rules by first measuring their value specific to the new data object. Experimental results show that our rule based classifiers outperform existing comparable classifiers in terms of accuracy and stability. Additionally, we test a number of pattern feature based models that use different kinds of patterns as features to represent each sequence as a feature vector. We then apply a variety of machine learning algorithms for sequence classification, experimentally demonstrating that the patterns we discover represent the sequences well, and prove effective for the classification task.

Because of a wide range of applications, e.g., GPS applications and location based services, spatial pattern discovery is an important task in data mining. A co-location pattern is defined as a subset of spatial items whose instances are often located together in spatial proximity. Current co-location mining algorithms are unable to quantify the spatial proximity of a co-location pattern. We propose a co-location pattern miner aiming to discover co-location patterns in a multidimensional spatial structure by measuring the cohesion of a pattern. We present two ways to build the co-location pattern miner, FromOne and FromAll, in an attempt to find a balance between accuracy and runtime. Additionally, we propose a method named Fre-ball to transform a structure into a transaction database, after which any existing itemset mining algorithm can be used to find the co-location patterns. An experimental evaluation shows that FromOne and Fre-ball are more efficient than existing methods. The usefulness of our methods is demonstrated by applying them on the publicly available geographical data of the city of Antwerp in Belgium.

Sequential pattern discovery is a well-studied field in data mining. Episodes are sequential patterns that describe events that often occur in the vicinity of each other. Episodes can impose restrictions on the order of the events, which makes them a versatile technique for describing complex patterns in the sequence. Most of the research on episodes deals with special cases such as serial and parallel episodes, while discovering general episodes is surprisingly understudied. This is particularly true when it comes to discovering association rules between them. In this paper we propose an algorithm that mines association rules between two general episodes. On top of the traditional definitions of frequency and confidence, we introduce two novel confidence measures for the rules. The major challenge in mining these association rules is pattern explosion. To limit the output, we aim to eliminate all redundant rules. We define the class of closed association rules, and show that this class contains all non-redundant output. To make the algorithm efficient, we use further pruning steps along the way. First of all, we generate only free and closed frequent episodes from which we create candidate rules, we speed up the evaluation of the rules, and finally prune the remaining non-closed rules from the output.

Sequential pattern discovery is a well-studied field in data mining. Episodes are sequential patterns describing events that often occur in the vicinity of each other. Episodes can impose restrictions to the order of the events, which makes them a versatile technique for describing complex patterns in the sequence. Most of the research on episodes deals with special cases such as serial, parallel, and injective episodes, while discovering general episodes is understudied. In this paper we extend the definition of an episode in order to be able to represent cases where events often occur simultaneously. We present an efficient and novel miner for discovering frequent and closed general episodes. Such a task presents unique challenges. Firstly, we cannot define closure based on frequency. We solve this by computing a more conservative closure that we use to reduce the search space and discover the closed episodes as a postprocessing step. Secondly, episodes are traditionally presented as directed acyclic graphs. We argue that this representation has drawbacks leading to redundancy in the output. We solve these drawbacks by defining a subset relationship in such a way that allows us to remove the redundant episodes. We demonstrate the efficiency of our algorithm and the need for using closed episodes empirically on synthetic and real-world datasets.

Discovering patterns in a sequence is an important aspect of data mining. One popular choice of such patterns are episodes, patterns in sequential data describing events that often occur in the vicinity of each other. Episodes also enforce in which order events are allowed to occur. In this work we introduce a technique for discovering closed episodes. Adopting existing approaches for discovering traditional patterns, such as closed item sets, to episodes is not straightforward. First of all, we cannot define a unique closure based on frequency because an episode may have several closed super episodes. Moreover, to define a closedness concept for episodes we need a subset relationship between episodes, which is not trivial to define. We approach these problems by introducing strict episodes. We argue that this class is general enough, and at the same time we are able to define a natural subset relationship within it and use it efficiently. In order to mine closed episodes we define an auxiliary closure operator. We show that this closure satisfies the needed Galois connection so that we can use the existing framework for mining closed patterns. Discovering the true closed episodes can be done as a post-processing step. We combine these observations into an efficient mining algorithm and demonstrate empirically its performance in practice.

Discovering interesting patterns in event sequences is a popular task in the field of data mining. Most existing methods try to do this based on some measure of cohesion to determine an occurrence of a pattern, and a frequency threshold to determine if the pattern occurs often enough. We introduce a new constraint based on a new interestingness measure combining the cohesion and the frequency of a pattern. For a dataset consisting of a single sequence, the cohesion is measured as the average length of the smallest intervals containing the pattern for each occurrence of its events, and the frequency is measured as the probability of observing an event of that pattern. We present a similar constraint for datasets consisting of multiple sequences. We present algorithms to efficiently identify the thus defined interesting patterns, given a dataset and a user-defined threshold. After applying our method to both synthetic and real-life data, we conclude that it indeed gives intuitive results in a number of applications.