Results 71 to 80 of about 2,962,161 (302)

Timed Sequential Pattern Mining Based on Confidence in Accumulated Intervals [PDF]

open access: yes, 2014
Many applications of sequential patterns require a guarantee of a particular event happening within a period of time. We propose CAI-PrefixSpan, a new data mining algorithm to obtain confident timed sequential patterns from sequential databases. Based on
Jou, Chichang   +1 more
core  

Non‐Additive Criteria Assessment in Sustainability Evaluation of High Entropy Alloys: A Toxicity‐Weighted Framework

open access: yesAdvanced Engineering Materials, EarlyView.
Sustainability assessment requires methodologies that appropriately distinguish between additive and non‐additive material properties. A toxicity‐weighted scoring system is developed and applied that accounts for the disproportionate influence of highly toxic constituents through nonlinear weighting functions, providing more realistic estimates than ...
Seth Mehalic   +2 more
wiley   +1 more source

Entropy‐Driven Design of Low‐Melting‐Point Alloys via Compositionally Complex Strategy

open access: yesAdvanced Engineering Materials, EarlyView.
Conventional low‐melting‐point alloys (LMPAs) are limited by a narrow compositional space and inherent property trade‐offs. This review presents an entropy‐driven design strategy that overcomes these limitations, ushering in a new class of low‐melting‐point compositionally complex alloys (LMCCAs).
Yinghui Shang   +6 more
wiley   +1 more source

Efficient periodicity mining of sequential patterns in a post-mining environment

open access: yes, 2008
Sequential pattern mining approaches mainly deal with finding the positive behaviour of a sequential pattern that can help in predicting the next event after a sequence of events.
Peneva, D.   +14 more
core   +1 more source

Sequential Mining: Patterns and Algorithms Analysis

open access: yesCoRR, 2013
This paper presents and analysis the common existing sequential pattern mining algorithms. It presents a classifying study of sequential pattern-mining algorithms into five extensive classes. First, on the basis of Apriori-based algorithm, second on Breadth First Search-based strategy, third on Depth First Search strategy, fourth on sequential closed ...
Thabet Slimani, Amor Lazzez
openaire   +2 more sources

Leveraging Symbolic Artificial Intelligence and Fuzzy Logic for Materials Science: A Review of Methods, Challenges, and Applications to Scarce and Imperfect Experimental Data

open access: yesAdvanced Engineering Materials, EarlyView.
This article explores the transformative potential of symbolic artificial intelligence (AI) in the field of materials science, particularly in leveraging experimental data. The article presents several symbolic AI models and discusses their applications in materials science.
Ahmed Amrani   +7 more
wiley   +1 more source

Finding frequent trajectories by clustering and sequential pattern mining

open access: yesJournal of Traffic and Transportation Engineering (English ed. Online), 2014
Data mining is a powerful emerging technology that helps to extract hidden information from a huge volume of historical data. This paper is concerned with finding the frequent trajectories of moving objects in spatio-temporal data by a novel method ...
Arthur A. Shaw, N.P. Gopalan
doaj   +1 more source

Discovery of Frequent Patterns of Episodes Within a Time Window for Alarm Management Systems

open access: yesIEEE Access, 2020
The sequential pattern mining field is expanding through numerous researches and has a large number of applications such as language processing, alarms management and event management on a broader scale.
Adel Hidri   +2 more
doaj   +1 more source

Interactive Constraint-Based Sequential Pattern Mining [PDF]

open access: yes, 2001
Data mining is an interactive and iterative process. It is very likely that a user will execute a series of similar queries di#ering in pattern constraints and mining parameters, before he or she gets satisfying results.
Marek Wojciechowski
core   +1 more source

A Multi‐Scale Machine Learning Framework for the Inverse Design of High Entropy Alloys

open access: yesAdvanced Engineering Materials, EarlyView.
High‐entropy alloys offer vast potential for various applications, including electrocatalysis; however, their compositional complexity challenges conventional screening. We introduce an inverse‐design framework combining two neural networks to determine optimal compositions and reconstruct nanoparticle geometry from targeted properties and conventional
Mikael Takoutsin   +14 more
wiley   +1 more source

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