scholarly journals Classification and Analysis of Go-Arounds in Commercial Aviation Using ADS-B Data

Aerospace ◽  
2021 ◽  
Vol 8 (10) ◽  
pp. 291
Author(s):  
Satvik G. Kumar ◽  
Samantha J. Corrado ◽  
Tejas G. Puranik ◽  
Dimitri N. Mavris
Keyword(s):  
San Francisco ◽  
Data Analytics ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Commercial Aviation ◽  
Safety Critical ◽  
Standard Procedures ◽  
Energy States ◽  
Insight Into ◽  
Gaining Insight

Go-arounds are a necessary aspect of commercial aviation and are conducted after a landing attempt has been aborted. It is necessary to conduct go-arounds in the safest possible manner, as go-arounds are the most safety-critical of operations. Recently, the increased availability of data, such as ADS-B, has provided the opportunity to leverage machine learning and data analytics techniques to assess aviation safety events. This paper presents a framework to detect go-around flights, identify relevant features, and utilize unsupervised clustering algorithms to categorize go-around flights, with the objective of gaining insight into aspects of typical, nominal go-arounds and factors that contribute to potentially abnormal or anomalous go-arounds. Approaches into San Francisco International Airport in 2019 were examined. A total of 890 flights that conducted a single go-around were identified by assessing an aircraft’s vertical rate, altitude, and cumulative ground track distance states during approach. For each flight, 61 features relevant to go-around incidents were identified. The HDBSCAN clustering algorithm was leveraged to identify nominal go-arounds, anomalous go-arounds, and a third cluster of flights that conducted a go-around significantly later than other go-around trajectories. Results indicate that the go-arounds detected as being anomalous tended to have higher energy states and deviations from standard procedures when compared to the nominal go-arounds during the first approach, prior to the go-around. Further, an extensive comparison of energy states between nominal flights, anomalous flights, the first approach prior to the go-around, and the second approach following the go-around is presented.

Customer Behavior Prediction using K-Means Clustering Algorithm

2016 ◽  
pp. 256-267 ◽  
Author(s):  
Juhi Singh ◽  
Mandeep Mittal ◽  
Sarla Pareek
Keyword(s):  
Association Rules ◽  
Clustering Algorithm ◽  
A Priori ◽  
Clustering Algorithms ◽  
Customer Behavior ◽  
Purchase Behavior ◽  
Behavior Prediction ◽  
Customer Data ◽  
Customers Segmentation ◽  
Insight Into

Due to the increased availability of individual customer data, it is possible to predict customer buying pattern. Customers can be segmented using clustering algorithms based on various parameters such as Frequency, Recency and Monetary values (RFM). The data can further be analyzed to infer rules among two or more purchases of the customer. In this chapter we will present a clustering algorithm, enhanced k- means algorithm, which is based on k- means algorithm to divide customers into various segments. After segmentation, each segment is mined with the help of a priori algorithm to infer rules so that the customer's purchase behavior can be predicted. From large number of association rules with sufficient coverage, the customer's purchasing pattern can be predicted. Experiment on real database is implemented to evaluate the performance on effectiveness and utility of the approach. The results show that the proposed approach can gain a well insight into customers' segmentation and thus their behavior can be predicted.

Handling WSD using Hierarchical Clustering Algorithm with sentences

2018 ◽  
pp. 83-88
Author(s):  
Mohana Priya K ◽  
Pooja Ragavi S ◽  
Krishna Priya G
Keyword(s):  
Hierarchical Clustering ◽  
Similarity Measure ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Cosine Similarity Measure ◽  
Hierarchical Clustering Algorithm ◽  
Multiple Levels ◽  
Pos Tagger ◽  
Sentence Clustering ◽  
The Right

Clustering is the process of grouping objects into subsets that have meaning in the context of a particular problem. It does not rely on predefined classes. It is referred to as an unsupervised learning method because no information is provided about the "right answer" for any of the objects. Many clustering algorithms have been proposed and are used based on different applications. Sentence clustering is one of best clustering technique. Hierarchical Clustering Algorithm is applied for multiple levels for accuracy. For tagging purpose POS tagger, porter stemmer is used. WordNet dictionary is utilized for determining the similarity by invoking the Jiang Conrath and Cosine similarity measure. Grouping is performed with respect to the highest similarity measure value with a mean threshold. This paper incorporates many parameters for finding similarity between words. In order to identify the disambiguated words, the sense identification is performed for the adjectives and comparison is performed. semcor and machine learning datasets are employed. On comparing with previous results for WSD, our work has improvised a lot which gives a percentage of 91.2%

DRSA: a non-hierarchical clustering algorithm using k-NN graph and its application in vegetation classification

2015 ◽  
pp. 125-138 ◽  
Author(s):  
I. V. Goncharenko
Keyword(s):  
Cluster Analysis ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Clustering Algorithms ◽  
Protein Structures ◽  
Hierarchical Cluster ◽  
Vegetation Classification ◽  
K Nearest Neighbor ◽  
Neighbor Graph ◽  
Nearest Neighbor Graph

In this article we proposed a new method of non-hierarchical cluster analysis using k-nearest-neighbor graph and discussed it with respect to vegetation classification. The method of k-nearest neighbor (k-NN) classification was originally developed in 1951 (Fix, Hodges, 1951). Later a term “k-NN graph” and a few algorithms of k-NN clustering appeared (Cover, Hart, 1967; Brito et al., 1997). In biology k-NN is used in analysis of protein structures and genome sequences. Most of k-NN clustering algorithms build «excessive» graph firstly, so called hypergraph, and then truncate it to subgraphs, just partitioning and coarsening hypergraph. We developed other strategy, the “upward” clustering in forming (assembling consequentially) one cluster after the other. Until today graph-based cluster analysis has not been considered concerning classification of vegetation datasets.

User Power Behavior Similarity Clustering Based on Unsupervised Extreme Learning Machine Algorithm

2020 ◽  
Vol 13 (5) ◽  
pp. 641-649
Author(s):  
Yuancheng Li ◽  
Yaqi Cui ◽  
Xiaolong Zhang
Keyword(s):  
Extreme Learning Machine ◽  
Clustering Algorithm ◽  
Characteristic Curve ◽  
Clustering Algorithms ◽  
Data Sets ◽  
Residential Areas ◽  
Processing Power ◽  
Learning Machine ◽  
Advanced Metering ◽  
Matlab Programming

Background: Advanced Metering Infrastructure (AMI) for the smart grid is growing rapidly which results in the exponential growth of data collected and transmitted in the device. By clustering this data, it can give the electricity company a better understanding of the personalized and differentiated needs of the user. Objective: The existing clustering algorithms for processing data generally have some problems, such as insufficient data utilization, high computational complexity and low accuracy of behavior recognition. Methods: In order to improve the clustering accuracy, this paper proposes a new clustering method based on the electrical behavior of the user. Starting with the analysis of user load characteristics, the user electricity data samples were constructed. The daily load characteristic curve was extracted through improved extreme learning machine clustering algorithm and effective index criteria. Moreover, clustering analysis was carried out for different users from industrial areas, commercial areas and residential areas. The improved extreme learning machine algorithm, also called Unsupervised Extreme Learning Machine (US-ELM), is an extension and improvement of the original Extreme Learning Machine (ELM), which realizes the unsupervised clustering task on the basis of the original ELM. Results: Four different data sets have been experimented and compared with other commonly used clustering algorithms by MATLAB programming. The experimental results show that the US-ELM algorithm has higher accuracy in processing power data. Conclusion: The unsupervised ELM algorithm can greatly reduce the time consumption and improve the effectiveness of clustering.

Standing up to the Demos

2018 ◽  
Author(s):  
Andrew Erskine
Keyword(s):  
Athenian Democracy ◽  
Parallel Lives ◽  
Standing Up ◽  
A Minor ◽  
Insight Into ◽  
Gaining Insight ◽  
The Way

Plutarch wrote twenty-three Greek Lives in his series of Parallel Lives—of these, ten were devoted to Athenians. Since Plutarch shared the hostile view of democracy of Polybius and other Hellenistic Greeks, this Athenian preponderance could have been a problem for him. But Plutarch uses these men’s handling of the democracy and especially the demos as a way of gaining insight into the character and capability of his protagonists. This chapter reviews Plutarch’s attitude to Athenian democracy and examines the way a statesman’s character is illuminated by his interaction with the demos. It also considers what it was about Phocion that so appealed to Plutarch, first by looking at his relationship with the democracy and then at the way he evokes the memory of Socrates. For him this was not a minor figure, but a man whose life was representative of the problems of Athenian democracy.

Pinball Loss Twin Support Vector Clustering

2021 ◽  
Vol 17 (2s) ◽  
pp. 1-23
Author(s):  
M. Tanveer ◽  
Tarun Gupta ◽  
Miten Shah ◽  
Keyword(s):  
Loss Function ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Structural Mri ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Support Vector Clustering ◽  
Hinge Loss ◽  
Pinball Loss ◽  
Vector Clustering

Twin Support Vector Clustering (TWSVC) is a clustering algorithm inspired by the principles of Twin Support Vector Machine (TWSVM). TWSVC has already outperformed other traditional plane based clustering algorithms. However, TWSVC uses hinge loss, which maximizes shortest distance between clusters and hence suffers from noise-sensitivity and low re-sampling stability. In this article, we propose Pinball loss Twin Support Vector Clustering (pinTSVC) as a clustering algorithm. The proposed pinTSVC model incorporates the pinball loss function in the plane clustering formulation. Pinball loss function introduces favorable properties such as noise-insensitivity and re-sampling stability. The time complexity of the proposed pinTSVC remains equivalent to that of TWSVC. Extensive numerical experiments on noise-corrupted benchmark UCI and artificial datasets have been provided. Results of the proposed pinTSVC model are compared with TWSVC, Twin Bounded Support Vector Clustering (TBSVC) and Fuzzy c-means clustering (FCM). Detailed and exhaustive comparisons demonstrate the better performance and generalization of the proposed pinTSVC for noise-corrupted datasets. Further experiments and analysis on the performance of the above-mentioned clustering algorithms on structural MRI (sMRI) images taken from the ADNI database, face clustering, and facial expression clustering have been done to demonstrate the effectiveness and feasibility of the proposed pinTSVC model.

scholarly journals An Enhanced Spectral Clustering Algorithm with S-Distance

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 596
Author(s):  
Krishna Kumar Sharma ◽  
Ayan Seal ◽  
Enrique Herrera-Viedma ◽  
Ondrej Krejcar
Keyword(s):  
Spectral Clustering ◽  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Clustering Algorithms ◽  
Rank Test ◽  
Customer Churn ◽  
Signed Rank ◽  
Signed Rank Test ◽  
Spectral Clustering Algorithm ◽  
Industrial Databases

Calculating and monitoring customer churn metrics is important for companies to retain customers and earn more profit in business. In this study, a churn prediction framework is developed by modified spectral clustering (SC). However, the similarity measure plays an imperative role in clustering for predicting churn with better accuracy by analyzing industrial data. The linear Euclidean distance in the traditional SC is replaced by the non-linear S-distance (Sd). The Sd is deduced from the concept of S-divergence (SD). Several characteristics of Sd are discussed in this work. Assays are conducted to endorse the proposed clustering algorithm on four synthetics, eight UCI, two industrial databases and one telecommunications database related to customer churn. Three existing clustering algorithms—k-means, density-based spatial clustering of applications with noise and conventional SC—are also implemented on the above-mentioned 15 databases. The empirical outcomes show that the proposed clustering algorithm beats three existing clustering algorithms in terms of its Jaccard index, f-score, recall, precision and accuracy. Finally, we also test the significance of the clustering results by the Wilcoxon’s signed-rank test, Wilcoxon’s rank-sum test, and sign tests. The relative study shows that the outcomes of the proposed algorithm are interesting, especially in the case of clusters of arbitrary shape.

scholarly journals Gaining Insight into Vitiligo Genetics through the Lens of a Large Epidemiologic Study

2021 ◽  
Vol 141 (4) ◽  
pp. 718-721
Author(s):  
Ken Okamura ◽  
Manuel Garber ◽  
John E. Harris
Keyword(s):  
Epidemiologic Study ◽  
Insight Into ◽  
Gaining Insight
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