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Unsupervised and Semi-supervised Machine Learning Algorithms for Cognitive IoT Systems
Published in Pethuru Raj, Anupama C. Raman, Harihara Subramanian, Cognitive Internet of Things, 2022
Pethuru Raj, Anupama C. Raman, Harihara Subramanian
The common characteristic is perhaps a relationship, connections, likelihood, etc. For instance, a customer who books a hotel would most likely need a rental car, so the chances of reserving a rental car are high. A few other examples could beA subgroup of diabetes patients grouped by their gene type and sequenceGroups of online viewers based on their time spent on a page/productMusic album ratings from music enthusiastsApriori algorithm for association rule learning problems – The Apriori algorithm is the first algorithm that is popular for frequent item set mining. It generates association rules by mining items, usually from a transactional database. As seen in the following Figure 6.9 this algorithm is beneficial for market-based analysis, where we need combinations of products that frequently co-occur in the database.
Cyber Securities Issues Fraud and Corruption
Published in Rodgers Waymond, Artificial Intelligence in a Throughput Model, 2020
The classical programs are the tools that are implemented in machine learning. They can be described as follows: Regression (or prediction)—a task of predicting the next value based on the previous values.Classification—a task of separating things into different categories.Clustering—it is similar to classification but the classes are unknown, and involves grouping things by their similarity.Association rule learning (or recommendation)—a task of recommending something based on previous experience.Dimensionality reduction—or generalization, a task of searching common and most important features in multiple examples.Generative models—a task of creating something based on the previous knowledge of the distribution.
Use of Association Rules for Cause-effects Relationships Analysis of Collision Accidents in the Yangtze River
Published in Adam Weintrit, Tomasz Neumann, Advances in Marine Navigation and Safety of Sea Transportation, 2019
B. Wu, J.H. Zhang, X.P. Yan, T.L. Yip
Association rule learning is a rule-based machine learning method for discovering hidden relationships between variables in a database from the perspective of data mining. When introducing it to ship collision accident analysis, after discovering the patterns of ship collision, it is meaningful to take countermeasures to cut off the necessary causation factors in an association rule. For example, an association rule for ship collisions in the Yangtze River is {accident area = anchorage} => {encounter scenarios = collision with stationary ship}. It shows that the collision with stationary ships will have a large probability to occur when ships are anchored at an anchorage. Therefore, the officer on watch should always take sharp lookout to prevent the occurrence of collision accidents when anchoring in the anchorage.
A meta data mining framework for botnet analysis
Published in International Journal of Computers and Applications, 2019
Afzalul Haque, Amrit Venkat Ayyar, Sanjay Singh
Apriori is an algorithm for frequent item set mining and association rule learning over databases. It proceeds by identifying the frequent individual items and extending them to larger and larger item sets as long as those item sets appear sufficiently often in the database. The frequent item sets determined can be used to determine association rules which highlight general trends in the database. We have modified and optimized the implementation of Apriori algorithm in JAVA. Our implementation uses JAVA’s BitSet class (which implements Bitmaps) to represent sets of items. Various set operations, mainly union, intersection and complement which traditionally would take asymptotic time are replaced with logical operations like OR, AND and XOR which take only O(1) asymptotic time, and only use a couple of CPU assembly instructions.
Data mining-based detection of the physical and chemical characteristics of Chinese medical herbs aqueous decoction in spray drying yield
Published in Drying Technology, 2021
Pandi Liu, Jiaxuan Li, Chenghao Lu, Lijie Zhao, Xiao Lin, Youjie Wang, Xuming Yang
Association natural combination is an important feature of association rules, which is useful to find existing subset modes of all attributes. Apriori algorithm is a classical association algorithm designed for frequent item set mining and association rule learning. Apriori algorithm has two main stages: First, identify all frequent item sets that appear sufficiently often, at least in line with the minimum support; second, generate strong association rules from the frequent item sets that satisfy both a minimum support threshold and a minimum confidence threshold.[25]
Applications of machine learning methods in traffic crash severity modelling: current status and future directions
Published in Transport Reviews, 2021
Xiao Wen, Yuanchang Xie, Liming Jiang, Ziyuan Pu, Tingjian Ge
Association rule learning, also known as the market basket analysis, is the identification of sets of items (e.g. factors in the context of crash severity) that occur together in one observation. Association rules are based on the relative frequency of factors occur alone and in combination in a dataset. It should be pointed out that association rules represent associations among factors but cannot be treated as indicators of causality.