Results 31 to 40 of about 3,318,937 (295)
MISFP-Growth: Hadoop-Based Frequent Pattern Mining with Multiple Item Support
Applied Sciences, 2019 In practice, single item support cannot comprehensively address the complexity of items in large datasets. In this study, we propose a big data analytics framework (named Multiple Item Support Frequent Patterns, MISFP-growth algorithm) that uses Hadoop ...
Chen-Shu Wang, Jui-Yen Chang
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AprioriAllPkg: A Python package for mining sequential patterns
SoftwareXThis work presents a software package designed to support transparency, education, and methodological reproducibility in sequential pattern mining.
Aleksander Kruczkowski, Pawel Weichbroth
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Sequence Mining Based Alarm Suppression
IEEE Access, 2018 Despite the high-pace improvement of industrial process automation, the management of abnormal events still requires human actions. Alarm systems are becoming crucial in providing situation-specific information to the decreasing number of operators.
Gyula Dorgo, Janos Abonyi
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Mining association rules for the quality improvement of the production process [PDF]
, 2013 Academics and practitioners have a common interest in the continuing development of methods and computer applications that support or perform knowledge-intensive engineering tasks.
Rigal, Fabien +2 more
core +1 more source
Frequent Pattern Mining for IoT Big Streaming Data Based on Short-Pattern Graph
Big Data Mining and AnalyticsInternet of things (IoT) technology is important in the era of big data, and in most IoT scenarios, data are constantly arriving in the form of streams.
Guang Yang, Xia Wu, Jing Zhang
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Mining frequent closed itemsets with the frequent pattern list [PDF]
Proceedings 2001 IEEE International Conference on Data Mining, 2002 The mining of a complete set of frequent itemsets will lead to a huge number of itemsets. Fortunately, this problem can be reduced to the mining of frequent closed itemsets (FCIs), which results in a much smaller number of itemsets. The approaches to mining frequent closed itemsets can be categorized into two groups: those with candidate generation and
Tseng, Fan-Chen +2 more
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Survey of differential privacy in frequent pattern mining
Tongxin xuebao, 2014 Frequent pattern mining is an exploratory problem in the field of data mining.However,directly releasing the discovered frequent patterns and the corresponding true supports may reveal the individuals’ privacy.The state-of-the-art solution for this ...
Li-ping DING, Guo-qing LU
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Engineering peptides into antibodies—opportunities and strategies for therapeutic innovation
FEBS Letters, EarlyView.Peptides and antibodies occupy complementary therapeutic niches. Peptides recognize difficult targets in a compact format, while antibodies add specificity, long half‐life, and effector functions. This review examines strategies that merge both modalities—peptide grafting into loops, terminal and Fc fusions, and bioconjugation—highlighting how ...
Jinling Wang +2 more
wiley +1 more source
Mining Frequent Route Patterns Based on Personal Trajectory Abstraction
IEEE Access, 2017 Frequent route pattern mining from personal trajectory data is the basis of location awareness and location services. However, because personal trajectory data is highly uncertain, most existing approaches are only capable of finding short and incomplete
Zhongliang Fu +3 more
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Pattern Mining in Frequent Dynamic Subgraphs [PDF]
Sixth International Conference on Data Mining (ICDM'06), 2006 Graph-structured data is becoming increasingly abundant in many application domains. Graph mining aims at finding interesting patterns within this data that represent novel knowledge. While current data mining deals with static graphs that do not change over time, coming years will see the advent of an increasing number of time series of graphs.
Karsten M. Borgwardt +2 more
openaire +4 more sources