Understanding software modelers’ difficulties and evaluating their performance is crucial to Model-Driven Engineering (MDE) education. The software modeling process contains fine-grained information about the modelers’ analysis and thought processes. ...

Childhood obesity is considered one of the main public health concerns. Research in the field of obesity detection and prevention is moving towards promising solutions thanks to the use of Artificial Intelligence applied to data from cohorts of ...

Community detection is a significant research area in social networks. Most methods use network topology, but combining it with user interactions improves accuracy. This paper proposes a robust method to identify communities based on the improved ...

Frequent pattern mining (FPM), whose goal is to identify patterns with appearance frequencies above a specified support threshold on a large graph, has attracted increasing attention owing to its diverse applications. The task suffers from an ...

• We introduce a motifs-based graph sampling algorithm that reserves salient features.
• We develop a support-threshold-free mining algorithm to identify top-B patterns.
• We built a neural network model to perform support estimation.

Frequent pattern mining is a relevant method to analyse structured data, like sequences, trees or graphs. It consists in identifying characteristic substructures of a dataset. This paper deals with a new type of patterns for tree data: common ...

Periodic frequent-pattern mining (PFPM) is a vital knowledge discovery technique that identifies periodically occurring patterns in a temporal database. Although traditional PFPM algorithms have many applications, they often produce a large set of ...

Data mining provides various frequent pattern mining methods to help business owners identify items with frequency or utility values greater than a particular threshold. Conversely, in the stock markets with a wide variety of stocks, a major ...

To address the time-dependent agile earth observation satellite (AEOS) scheduling problem more effectively, the frequent pattern-based parallel search (FPBPS) algorithm is proposed, which is composed of a parallel local search ...

Mining frequent Time Intervals-Related Patterns (TIRPs) from series of symbolic time intervals offers a comprehensive framework for heterogeneous, multivariate temporal data analysis in various application domains. While gaining a growing interest ...

• The first method for interactive mining of Top-K uncertain frequent patterns.
• ...

Top-K Uncertain Frequent Pattern (UFP) mining is an interesting topic in data mining. The existing TUFP algorithm supports static mining of Top-K UFPs; however, in the real world, users need to repeatedly change the K threshold to ...

In multi axis NC machining, there is a certain security risk in wireless data transmission. A security risk prediction model for wireless data transmission in multi axis NC machining is designed. Through frequent pattern mining, multi axis NC ...

Call Detail Record (CDR) is the detailed record of all the telephonic calls that pass through a telephone exchange or any other telecommunications equipment. It contains temporal and spatial data, and can also convey other information that would ...

Frequent pattern mining (FPM) on large graphs has received more and more attention due to its importance in various applications, including social media analysis. The FPM models are designed to find the patterns in a large graph with a ...

• This study proposes a near-optimal top-k pattern mining approach.
• Computational ...

Frequent pattern mining is a popular technique in big data mining and analytics. It discovers frequently occurring sets of items (e.g., popular merchandise items, frequently co-occurring events) from big data found in numerous database engineered ...

As a popular big data analytics and knowledge discovery task, frequent pattern mining aims to discover frequently occurring sets of items (e.g., merchandise items, events) from big data. Frequent patterns can be discovered horizontally by ...

Frequent Pattern Mining (FPM) is a classical graph mining task. In recent years, we have witnessed wide applications of FPM. However, emerging applications keep calling for more expressive patterns to capture more complex structures in a large ...

• The Kernel Possibilistic Fuzzy Local Information C-Means (KPFLICM) clustering and K-means clustering-based feature selection algorithm for association rules (...

Frequent Pattern Mining (FPM) is regularly used in data mining applications for identifying objects of interest that frequent transactional databases. Traditional ARM algorithms work on minimum levels of support and confidence metrics ...

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Discrete sequential data is the collection of an ordered series of discrete events or abstractions from a set of data points collected at different time periods. These processes are one of the most common and important process types ...

• Mining frequent patterns from multivariate time series requires high memory usage.

Simplicial complexes are a generalization of graphs that model higher-order relations. In this paper, we introduce simplicial patterns —that we call simplets— and generalize the task of frequent pattern mining from the realm of graphs to that of ...

• We propose a parallel depth-first search with dynamic load balancing.
• We ...

In the Internet age, analyzing the behavior of online users can help webstore owners understand customers’ interests. Insights from such analysis can be used to improve both user experience and website design. A prominent task for ...