Estimation of Column Aerosol Contribution in Seoul and Gangneung Using Machine Learning Clustering Technique

2021 ◽  
Vol 37 (6) ◽  
pp. 931-945
Author(s):  
Seong-Hun Pyo ◽  
Kwon-Ho Lee ◽  
Kyu-Tae Lee
Keyword(s):  
Machine Learning ◽  
Clustering Technique

scholarly journals Machine‐Learning Clustering Technique Applied to Powder X‐Ray Diffraction Patterns to Distinguish Compositions of ThMn 12 ‐Type Alloys

2020 ◽  
Vol 3 (7) ◽  
pp. 2000039
Author(s):  
Keishu Utimula ◽  
Rutchapon Hunkao ◽  
Masao Yano ◽  
Hiroyuki Kimoto ◽  
Kenta Hongo ◽  
...  
Keyword(s):  
Machine Learning ◽  
X Ray Diffraction ◽  
X Ray ◽  
Clustering Technique ◽  
Diffraction Patterns

scholarly journals Data Mining Approach for Customer Segmentation

2021 ◽  
Vol 9 (VI) ◽  
pp. 1008-1012
Author(s):  
Anshumala Jaiswal
Keyword(s):  
Machine Learning ◽  
Clustering Algorithm ◽  
Customer Segmentation ◽  
Machine Learning Algorithms ◽  
Clustering Methods ◽  
Clustering Technique ◽  
Data Mining Approach ◽  
Huge Data ◽  
Or Groups ◽  
Data Points

In Marketing world, rapidly increasing competition makes it difficult to sustain in this field, marketers have to take decisions that satisfy their customers. Growth of an organization is highly depended on right decisions by the organization. For that, they have to collect deep knowledge about their customer's needs. Substantial amount of data of customers is collected daily. To manage such a huge data is not a piece of cake. An idea is to segment customers in different groups and go through each group and find the potential group among pool of customers. If it is done manually, it will require lot of human efforts and also consume lot of time. For reducing the human efforts, machine learning plays an important role. One can find various patterns which is used to analyze customers database using machine learning algorithms. Using clustering technique, customers can be segmented on the basis of some similarities. One of the best procedures for clustering technique is by using K-means algorithm. The k-means clustering algorithm is one of the widely used data clustering methods where the datasets having “n” data points are partitioned into “k” groups or cluster [1].in this paper. K is number of clusters or groups or segments and elbow method is used for determining value of K.

scholarly journals Analysis and Prediction of Stock Market Mining using Machine Learning Clustering Technique

2021 ◽  
Vol 183 (7) ◽  
pp. 39-44
Author(s):  
Zahraa Elsayed Mohamed ◽  
El-Amin Kamal El-Din El-Mesalamy
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Clustering Technique

scholarly journals Friend Recommendation using Unsupervised Machine Learning

2020 ◽  
Vol 9 (8) ◽  
pp. 5-8
Keyword(s):  
Machine Learning ◽  
Hierarchical Clustering ◽  
Principle Component Analysis ◽  
Component Analysis ◽  
Unsupervised Machine Learning ◽  
Principle Component ◽  
Clustering Technique ◽  
Friend Recommendation ◽  
The Absolute

Friend recommendation is one of a lot of accepted characteristics of amusing arrangement platforms, which recommends agnate or accustomed humans to users. The abstraction of friend recommendation originates from amusing networks such as Twitter and Facebook, which uses friends-offriends adjustment to acclaim people. We can say users do not accomplish accompany from accidental humans but end up authoritative accompany with their friends’ friends. The absolute methods accept attenuated ambit of recommendation and are beneath efficient. Here in our proposed access, we are applying an added hierarchical clustering technique with the collaborative clarification advocacy algorithm as well the Principle Component Analysis (PCA) adjustment is activated for abbreviation the ambit of abstracts to get added accurateness in the results. The hierarchical clustering will accommodate added allowances of the clustering technique over the dataset, and the PCA will adviseredefining the dataset by abbreviating the ambit of the dataset as required. By implementing the above appearance of these two techniques on the acceptable collaborative clarification advocacy algorithm, the above apparatus acclimated for recommendations can be improved

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Machine Learning for Speaker Recognition

2020 ◽  
Author(s):  
Man-Wai Mak ◽  
Jen-Tzung Chien
Keyword(s):  
Machine Learning ◽  
Speaker Recognition
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