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Chan-Uk Yeom and Keun-Chang Kwak
In this paper, we propose context-based GK clustering and design a CGK-based granular model and a hierarchical CGK-based granular model. Existing fuzzy clustering generates clusters using Euclidean distances. However, there is a problem in that performan...
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Franz Aurenhammer, Christoph Ladurner and Michael Steinkogler
We show that the so-called motorcycle graph of a planar polygon can be constructed by a randomized incremental algorithm that is simple and experimentally fast. Various test data are given, and a clustering method for speeding up the construction is prop...
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Xiaolin Chen, Qixing Qu, Chengxi Wei and Shudong Chen
The significance of research on public opinion monitoring of social network emergencies is becoming increasingly important. As a platform for users to communicate and share information online, social networks are often the source of public opinion about ...
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Vicente Román, Luis Payá, Sergio Cebollada and Óscar Reinoso
In this work, an incremental clustering approach to obtain compact hierarchical models of an environment is developed and evaluated. This process is performed using an omnidirectional vision sensor as the only source of information. The method is structu...
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Chan-Uk Yeom and Keun-Chang Kwak
We propose an adaptive neuro-fuzzy inference system (ANFIS) with an incremental tree structure based on a context-based fuzzy C-means (CFCM) clustering process. ANFIS is a combination of a neural network with the ability to learn, adapt and compute, and ...
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Marian B. Gorzalczany and Filip Rudzinski
In this paper, we briefly present several modifications and generalizations of the concept of self-organizing neural networks?usually referred to as self-organizing maps (SOMs)?to illustrate their advantages in applications that range from high-dimension...
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Yaming Wang, Zhengheng Xu, Wenqing Huang, Yonghua Han and Mingfeng Jiang
Traditional approaches to modeling and processing discrete pixels are mainly based on image features or model optimization. These methods often result in excessive shrinkage or expansion of the restored pixel region, inhibiting accurate recovery of the t...
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Athanasios Davvetas, Iraklis A. Klampanos, Spiros Skiadopoulos and Vangelis Karkaletsis
Evidence transfer for clustering is a deep learning method that manipulates the latent representations of an autoencoder according to external categorical evidence with the effect of improving a clustering outcome. Evidence transfer?s application on clus...
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Jian Zhang, Yaozong Pan, Ruili Wang, Yuqiang Fang and Haitao Yang
Decentralized partially observable Markov decision processes (Dec-POMDPs) are general multi-agent models for planning under uncertainty, but are intractable to solve. Doubly exponential growth of the search space as the horizon increases makes a brute-fo...
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Nitin patidar,Kushboo patidar
The management and analysis of big data has been recognized as one of the majority significant promising requirements in recent years. This is because of the pure volume and growing complexity of data creature created or composed. Existing clustering alg...
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