Journal ArticleDOI
A neuro-fuzzy Kohonen network for data stream possibilistic clustering and its online self-learning procedure
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TLDR
A modified two-layer neuro-fuzzy Kohonen network is used for solving the possibilistic fuzzy clustering tasks and this system tunes centers’ coordinates and membership levels of every pattern to clusters during the self-learning procedure and automatically increases a number of neurons during data processing.About:
This article is published in Applied Soft Computing.The article was published on 2017-10-01. It has received 27 citations till now. The article focuses on the topics: Fuzzy clustering & Data stream clustering.read more
Citations
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Journal ArticleDOI
Representing complex intuitionistic fuzzy set by quaternion numbers and applications to decision making
Roan Thi Ngan,Roan Thi Ngan,Le Hoang Son,Mumtaz Ali,Dan E. Tamir,Naphtali Rishe,Abraham Kandel +6 more
TL;DR: This paper generalize and expand the utility of complex intuitionistic fuzzy sets using the space of quaternion numbers, which can capture composite features and convey multi-dimensional fuzzy information via the functions of real membership, imaginary membership, real non-membership, and imaginary non- membership.
Journal ArticleDOI
Compression of results of geodetic displacement measurements using the PCA method and neural networks
TL;DR: The results of calculations carried out using artificial intelligence assisted and PCA indicates that the approach can be effectively used to compress of geodetic measurement results and then to reproduce them without loss of accuracy of displacement identification.
Journal ArticleDOI
GBK-means clustering algorithm: An improvement to the K-means algorithm based on the bargaining game
TL;DR: In this paper, a game-based k-means (GBK) algorithm is proposed, where cluster centers compete with each other to attract the largest number of similar objectives or entities to their cluster.
Book ChapterDOI
Imbalance Data Classification via Neural-Like Structures of Geometric Transformations Model: Local and Global Approaches
TL;DR: The article describes a new classification method based on neural-like structures of Geometric Transformations Model (local and global approaches) and compares their result with the obtained results.
Book ChapterDOI
Missing Data Imputation Through SGTM Neural-Like Structure for Environmental Monitoring Tasks
TL;DR: It is shown that the above-mentioned imputation methods in data monitoring of air pollution do not allow to obtain reliable results due to the low prediction accuracy, and experimentally established that the data imputation method through SGTM neural-like structure has a three times higher accuracy than the arithmetic mean algorithm.
References
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Book
Data Mining: Concepts and Techniques
TL;DR: This book presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects, and provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data.
Book
Data Mining: Practical Machine Learning Tools and Techniques
TL;DR: This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.
Book
Pattern Recognition with Fuzzy Objective Function Algorithms
TL;DR: Books, as a source that may involve the facts, opinion, literature, religion, and many others are the great friends to join with, becomes what you need to get.
Book
Self-Organizing Maps
TL;DR: The Self-Organising Map (SOM) algorithm was introduced by the author in 1981 as mentioned in this paper, and many applications form one of the major approaches to the contemporary artificial neural networks field, and new technologies have already been based on it.
Journal ArticleDOI
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
Alexander Strehl,Joydeep Ghosh +1 more
TL;DR: This paper introduces the problem of combining multiple partitionings of a set of objects into a single consolidated clustering without accessing the features or algorithms that determined these partitionings and proposes three effective and efficient techniques for obtaining high-quality combiners (consensus functions).