Fuzzy Associative Classification Driven MapReduce Computing Solution for Effective Learning from Uncertain and Dynamic Big Data

There is a collection of a large amount of automatic identification system (AIS) data that contains ship encounter information, but mining the collision avoidance knowledge from AIS big data and carrying out effective machine learning is a difficult problem in current maritime field. Herein, first the Douglas–Peucker (DP) algorithm was used to preprocess the AIS data. Then, based on the ship domain the risk of collision was identified. Finally, a double-gated recurrent unit neural network (GRU-RNN) was constructed to learn unmanned surface vehicle (USV) collision avoidance decision from the extracted data of successful encounters of ships. The double GRU-RNN was trained on the 2015 Tianjin Port AIS dataset to realize the effective learning of ship encounter data. The results indicated that the double GRU-RNN could effectively learn the collision avoidance pattern hidden in AIS big data, and generate corresponding ship collision-avoidance decisions for different maritime navigation states. This study contributes significantly to the increased efficiency and safety of sea operations. The proposed method could be potentially applied to USV technology and intelligence collision avoidance.

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A MapReduce solution for associative classification of big data

Information Sciences ◽

10.1016/j.ins.2015.10.041 ◽

2016 ◽

Vol 332 ◽

pp. 33-55 ◽

Cited By ~ 66

Author(s):

Alessio Bechini ◽

Francesco Marcelloni ◽

Armando Segatori

Keyword(s):

Big Data ◽

Associative Classification

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A Grammar-Guided Genetic Programing Algorithm for Associative Classification in Big Data

Cognitive Computation ◽

10.1007/s12559-018-9617-2 ◽

2019 ◽

Vol 11 (3) ◽

pp. 331-346 ◽

Cited By ~ 6

Author(s):

F. Padillo ◽

J. M. Luna ◽

S. Ventura

Keyword(s):

Big Data ◽

Associative Classification

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Reliable Distributed Fuzzy Discretizer for Associative Classification of Big data

International Journal of Information Retrieval Research ◽

10.4018/ijirr.289572 ◽

2022 ◽

Vol 12 (1) ◽

pp. 0-0

Keyword(s):

Data Mining ◽

Big Data ◽

Associative Classification ◽

Data Set ◽

Digital World ◽

Huge Data ◽

Fuzzy Partitioning ◽

Discrete Values ◽

Associative Classifiers

Data Mining is an essential task because the digital world creates huge data daily. Associative classification is one of the data mining task which is used to carry out classification of data, based on the demand of knowledge users. Most of the associative classification algorithms are not able to analyze the big data which are mostly continuous in nature. This leads to the interest of analyzing the existing discretization algorithms which converts continuous data into discrete values and the development of novel discretizer Reliable Distributed Fuzzy Discretizer for big data set. Many discretizers suffer the problem of over splitting the partitions. Our proposed method is implemented in distributed fuzzy environment and aims to avoid over splitting of partitions by introducing a novel stopping criteria. Proposed discretization method is compared with existing distributed fuzzy partitioning method and achieved good accuracy in the performance of associative classifiers.

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Rough Sets Base Associative Classification Rules Extraction from Big Data

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.a9140.119119 ◽

2019 ◽

Vol 9 (1) ◽

pp. 3096-3102

Keyword(s):

Big Data ◽

Data Analytics ◽

Extraction Process ◽

Classification Rules ◽

Associative Classification ◽

Mapreduce Framework ◽

Data Set ◽

Suggested Approach ◽

Standard Data ◽

Rules Extraction

Big Data is a current burning challenge for the data analytics research community. Many conventional data analytics techniques have been extended to the MapReduce framework to process Big Data. But in our literature review, we find that for the MapReduce system there is an absolute lack of rough setbased technique. To facilitate this and recognize the importance of the rule-based classification techniques, we suggest a roughset associative classification rules extraction process for the MapReduce framework. The implementation and evaluation of the Big Data Standard data set demonstrated the efficiency of our suggested approach.

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Evaluating associative classification algorithms for Big Data

Big Data Analytics ◽

10.1186/s41044-018-0039-7 ◽

2019 ◽

Vol 4 (1) ◽

Cited By ~ 1

Author(s):

Francisco Padillo ◽

José María Luna ◽

Sebastián Ventura

Keyword(s):

Big Data ◽

Classification Algorithms ◽

Associative Classification

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Reflections on Adaptive Learning Analytics

Advancing the Power of Learning Analytics and Big Data in Education - Advances in Educational Technologies and Instructional Design ◽

10.4018/978-1-7998-7103-3.ch003 ◽

2021 ◽

pp. 61-84

Author(s):

Ahmet Dogukan Sarıyalçınkaya ◽

Hasan Karal ◽

Fahriye Altinay ◽

Zehra Altinay

Keyword(s):

Big Data ◽

Adaptive Learning ◽

Learning Analytics ◽

Learning Opportunities ◽

Intervention Model ◽

Model Learning ◽

Individualized Learning ◽

Learning And Teaching ◽

Effective Learning ◽

Structured Intervention

Learning analytics is developed from the big data approach and plays an important role in the adaptive learning model. Learning analytics is individualized to provide more effective learning experiences and opportunities. Learning analytics can support learning and teaching a structured intervention model developed for those learning to improve their performance. This research chapter explains the two concepts from general to specific also the imperatives and distinctions between the two concepts. This chapter reveals that adaptive learning analytics can be defined as a subset of learning analytics that provides content to provide learners with more effective and adaptive learning opportunities. Learning analytics which is associated with adaptive learning calls upon adaptive learning analytics to create accurate individualized learning.

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