Unsupervised learning of mixture models based on swarm intelligence and neural networks with optimal completion using incomplete data

Abas, Ahmed R.

doi:10.1016/j.eij.2012.03.002

Cited by 5 publications

(3 citation statements)

References 17 publications

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“…Ghahramani and Jordan dealt with incomplete data via the EM approach from a supervised learning perspective . Abas combined local tuning of the general regression, particle swarm optimization (PSO) with EM to handle incomplete data set with missing values . Lin and Su proposed the EM and Bayesian classifier hybrid algorithm to solve the problem of incomplete data in feature extraction .…”

Section: Introductionmentioning

confidence: 99%

“…15 Abas combined local tuning of the general regression, particle swarm optimization (PSO) with EM to handle incomplete data set with missing values. 16,17 Lin and Su proposed the EM and Bayesian classifier hybrid algorithm to solve the problem of incomplete data in feature extraction. 18 Wang et al imputed missing data based on the EM algorithm, and then solved the nonparametric spectral analysis.…”

Section: Introductionmentioning

confidence: 99%

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A Global Clustering Approach Using Hybrid Optimization for Incomplete Data Based on Interval Reconstruction of Missing Value

Zhang

Liu

et al. 2015

Int. J. Intell. Syst.

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Incomplete data clustering is often encountered in practice. Here the treatment of missing attribute value and the optimization procedure of clustering are the important factors impacting the clustering performance. In this study, a missing attribute value becomes an information granule and is represented as a certain interval. To avoid intervals determined by different cluster information, we propose a congeneric nearest‐neighbor rule‐based architecture of the preclassification result, which can improve the effectiveness of estimation of missing attribute interval. Furthermore, a global fuzzy clustering approach using particle swarm optimization assisted by the Fuzzy C‐Means is proposed. A novel encoding scheme where particles are composed of the cluster prototypes and the missing attribute values is considered in the optimization procedure. The proposed approach improves the accuracy of clustering results, moreover, the missing attribute imputation can be implemented at the same time. The experimental results of several UCI data sets show the efficiency of the proposed approach.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Global Clustering Approach Using Hybrid Optimization for Incomplete Data Based on Interval Reconstruction of Missing Value

Zhang

Liu

et al. 2015

Int. J. Intell. Syst.

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“…environments [131][132][133][134][135][136]. There exist various clustering techniques including K-means [137], mixture models [138], hierarchical clustering [139], self-organizing map [103], and adaptive resonance theory [140].…”

Section: Resultsmentioning

confidence: 99%

Artificial intelligence based event detection in wireless sensor networks

Bahrepour¹

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Wireless sensor networks (WSNs) are composed of large number of small, inexpensive devices, called sensor nodes, which are equipped with sensing, processing, and communication capabilities. While traditional applications of wireless sensor networks focused on periodic monitoring, the focus of more recent applications is on fast and reliable identification of out-of-ordinary situations and events. This new functionality of wireless sensor networks is known as event detection. Due to the fact that collecting all sensor data centrally to perform event detection is inefficient in many occasions, the new trend in event detection in wireless sensor networks is to perform detection in the network. Design of in-network event detection methods for wireless sensor networks is by no means straightforward, as it needs to efficiently cope with various challenges and concerns including unreliability, heterogeneity, adaptability, and resource constraints. In this thesis, we tackle this problem by proposing fast, accurate, innetwork, and intelligent event detection methods using artificial intelligence (AI) and machine learning (ML) approaches. To this end, the main objective of this thesis is to analyze, investigate applicability, and optimize artificial intelligence (AI) and machine learning (ML) methods for efficient, distributed, local and in-network event detection in wireless sensor networks (WSNs). The main contributions of the thesis can be summarized as: Feature extraction and reduction in three datasets for event detection in WSNs. We choose three datasets containing sensory data from real V Acknowledgment Give light, and the darkness will disappear of itself. Desiderius Erasmus (1466-1536) known as Erasmus of Rotterdam, Dutch Renaissance humanist, Catholic priest, social critic, teacher, and theologian.

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A hybrid clustering algorithm based on missing attribute interval estimation for incomplete data

Zhang

Bing

Zhang

2014

Pattern Anal Applic

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Unsupervised learning of mixture models based on swarm intelligence and neural networks with optimal completion using incomplete data

Cited by 5 publications

References 17 publications

A Global Clustering Approach Using Hybrid Optimization for Incomplete Data Based on Interval Reconstruction of Missing Value

A Global Clustering Approach Using Hybrid Optimization for Incomplete Data Based on Interval Reconstruction of Missing Value

Artificial intelligence based event detection in wireless sensor networks

A hybrid clustering algorithm based on missing attribute interval estimation for incomplete data

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