What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Machine learning
Miin-Shen Yang mostly deals with Fuzzy logic, Cluster analysis, Fuzzy clustering, Fuzzy set and Pattern recognition.
His studies deal with areas such as Computational complexity theory and Similitude as well as Fuzzy logic.
His Cluster analysis study deals with Algorithm intersecting with FLAME clustering and Data mining.
As part of one scientific family, Miin-Shen Yang deals mainly with the area of Fuzzy clustering, narrowing it down to issues related to the Fuzzy number, and often Fuzzy classification and Error function.
The Fuzzy set study combines topics in areas such as Similarity, Similarity measure and Fuzzy control system.
His Pattern recognition study integrates concerns from other disciplines, such as Robust learning, Selection and Artificial intelligence.
His most cited work include:
- Similarity measures of intuitionistic fuzzy sets based on L p metric (144 citations)
- A similarity measure of intuitionistic fuzzy sets based on the Sugeno integral with its application to pattern recognition (106 citations)
- Cluster analysis based in fuzzy relations (91 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Cluster analysis, Artificial intelligence, Fuzzy logic, Fuzzy clustering and Pattern recognition.
His Cluster analysis study combines topics from a wide range of disciplines, such as Algorithm, Data mining and Data set.
His study focuses on the intersection of Artificial intelligence and fields such as Machine learning with connections in the field of Hypersphere.
His work in the fields of Fuzzy logic, such as Fuzzy set, intersects with other areas such as Generalization.
Miin-Shen Yang interconnects Mathematical optimization, Fuzzy control system and Mahalanobis distance in the investigation of issues within Fuzzy clustering.
Miin-Shen Yang regularly ties together related areas like k-means clustering in his Pattern recognition studies.
He most often published in these fields:
- Cluster analysis (60.34%)
- Artificial intelligence (50.00%)
- Fuzzy logic (47.41%)
What were the highlights of his more recent work (between 2017-2021)?
- Cluster analysis (60.34%)
- Fuzzy logic (47.41%)
- Artificial intelligence (50.00%)
In recent papers he was focusing on the following fields of study:
His main research concerns Cluster analysis, Fuzzy logic, Artificial intelligence, Fuzzy set and Pattern recognition.
His study in Cluster analysis is interdisciplinary in nature, drawing from both Algorithm, Data mining, Feature and Data set.
His work on Fuzzy number and Vagueness as part of his general Fuzzy logic study is frequently connected to Reliability and Pythagorean theorem, thereby bridging the divide between different branches of science.
His work deals with themes such as Fuzzy set operations and Fuzzy classification, which intersect with Fuzzy number.
His Fuzzy set research is multidisciplinary, incorporating elements of Computational intelligence, TOPSIS, Similarity, Hausdorff distance and Unit interval.
His research integrates issues of CURE data clustering algorithm, Correlation clustering, k-medians clustering, Fuzzy clustering and Computational complexity theory in his study of Pattern recognition.
Between 2017 and 2021, his most popular works were:
- A Feature-Reduction Fuzzy Clustering Algorithm Based on Feature-Weighted Entropy (49 citations)
- New similarity measures of intuitionistic fuzzy sets based on the Jaccard index with its application to clustering (40 citations)
- Unsupervised K-Means Clustering Algorithm (22 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Machine learning
His primary scientific interests are in Cluster analysis, Artificial intelligence, Pattern recognition, Initialization and k-means clustering.
The concepts of his Cluster analysis study are interwoven with issues in Algorithm and Data mining.
His studies in Algorithm integrate themes in fields like Canopy clustering algorithm and FLAME clustering.
His research in Fuzzy clustering, k-medians clustering, Correlation clustering, CURE data clustering algorithm and Similarity are components of Artificial intelligence.
The Pattern recognition study combines topics in areas such as Computational complexity theory, Intuitionistic fuzzy and Selection.
His k-means clustering research incorporates themes from Feature, Schema, Reduction, Unsupervised learning and Pattern recognition.
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