This paper proposed clustering algorithms applied Gaussian basis function neural network compensator with fuzzy control for magnetic bearing system (MBS). The nonlinear MBS improved traditional bearing friction losses...
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ISBN:
(纸本)9781509024407
This paper proposed clustering algorithms applied Gaussian basis function neural network compensator with fuzzy control for magnetic bearing system (MBS). The nonlinear MBS improved traditional bearing friction losses, and nonlinear system with fuzzy controller and neural network does not require precise MBS mathematical model. We used clustering algorithms which are fuzzy c-means and k-means adjusted Gaussian basis function in neural network. Finally, we used the Lyapunov stability to guarantee MBS convergence, and the experimental results shows proposed algorithm has satisfactory performance in MBS.
Wireless sensor networks (WSNs) have many applications in military services, health centers, industries as well as home surveillances. In such networks energy efficiency of nodes and life time of network are main conc...
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ISBN:
(纸本)9789380544168
Wireless sensor networks (WSNs) have many applications in military services, health centers, industries as well as home surveillances. In such networks energy efficiency of nodes and life time of network are main concerns. Different clustering approaches are used to efficiently optimize the energy of sensor nodes. clustering also improves the scalability of sensor nodes. We reviewed different approaches of clustering which are centralized, distributed and hybrid used in Sensor Networks. Recently there have been many researches on developing algorithms using equal and unequal clustering techniques. These techniques use residual energy of nodes and distance to base station as parameters for selecting cluster heads. This paper aims to examine various distributed and hybrid clustering algorithm as on date reported by different authors actively working in this area. We also briefly discuss the operations of these algorithms, as well as compare on the basis of various clustering attributes.
Data mining is the process of discovering knowledge from the vast data sources. In Data mining, classification and clustering are the two broad branches of study. In clustering, K-means algorithm is one of the bench m...
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The main goal of clustering algorithms is to organize a given set of data patterns into groups (clusters) and their main strategy is to group patterns based on their similarity. However, some clustering algorithms als...
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ISBN:
(纸本)9781509035670
The main goal of clustering algorithms is to organize a given set of data patterns into groups (clusters) and their main strategy is to group patterns based on their similarity. However, some clustering algorithms also require as an input parameter, the number of clusters the induced clustering should have, or then, a threshold value used for limiting for the number of induced clusters. Both, the number of cluster as well a threshold value are often unknown, however it is well-known that results of clustering tasks can be very sensitive to them. This work presents a method for empirically estimating both values. The method is based on multiple runs of sequential clustering algorithms, by using increasing threshold values. Results from experiments conducted using several data domains from two repositories, the UCI and the Keel, as well as a few artificially created data, are presented and a comparative analysis is carried out, as evidence of the good estimates on both values given by the method.
Data mining is the method which is useful for extracting useful information and data is extorted, but the classical data mining approaches cannot be directly used for big data due to their absolute complexity. The dat...
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ISBN:
(纸本)9781509011124
Data mining is the method which is useful for extracting useful information and data is extorted, but the classical data mining approaches cannot be directly used for big data due to their absolute complexity. The data that is been formed by numerous scientific applications and incorporated environment has grown rapidly not only in size but also in variety in recent era. The data collected is of very large amount and there is difficulty in collecting and assessing big data. clustering algorithms have developed as a powerful meta learning tool which can precisely analyze the volume of data produced by modern applications. The main goal of clustering is to categorize data into clusters such that objects are grouped in the same cluster when they are “similar” according to similarities, traits and behavior. The most commonly used algorithm in clustering are partitioning, hierarchical, grid based, density based, and model based algorithms. A review of clustering and its different techniques in data mining is done considering the criteria's for big data. Where most commonly used and effective algorithms like K-Means, FCM, BIRCH, CLIQUE algorithms are studied and compared on big data perspective.
Amount and diversity of data produced and processed has been dramatically increased parallel to improvements in technology. Unfortunately produced data usually don't have any labels which may make the classificati...
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Amount and diversity of data produced and processed has been dramatically increased parallel to improvements in technology. Unfortunately produced data usually don't have any labels which may make the classification and building information process more easily. This resulted with higher importance on data clustering for builing information. In this work K-Means, Spectral clustering and Girvan-Newman algorithms has been studied and compared on Breaast Cancer Wisconsin Data Set (BCWDS).
Among the power system corrective controls, defensive islanding is considered as the last resort to secure the system from severe cascading contingencies. The primary motive of defensive islanding is to limit the affe...
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ISBN:
(纸本)9781509033591
Among the power system corrective controls, defensive islanding is considered as the last resort to secure the system from severe cascading contingencies. The primary motive of defensive islanding is to limit the affected areas to maintain the stability of the resulting subsystems and to reduce the total loss of load in the system. The slow coherency based islanding can successfully be applied for the defensive islanding. In this paper, two partitioning methods are proposed, K-means clustering algorithm and fuzzy relational eigenvector centrality-based clustering algorithm. The proposed methods are using the data measured by phasor measurement units to determine the islands to be used in the defensive islanding. The proposed methods are demonstrated on the 16-generator 68-bus power system and their performances are discussed as their results are compared.
Big data is a main problem for data mining methods. Fortunately, the rapid advances in affordable high performance computing platforms such as the Graphics Processing Unit (GPU) have helped researchers in reducing the...
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ISBN:
(纸本)9781509043217
Big data is a main problem for data mining methods. Fortunately, the rapid advances in affordable high performance computing platforms such as the Graphics Processing Unit (GPU) have helped researchers in reducing the execution time of many algorithms including data mining algorithms. This paper discusses the utilization of the parallelism capabilities of the GPU to improve the performance of two common clustering algorithms, which are K-Means (KM) and Fuzzy C-Means (FCM) algorithms. Two main parallelism approaches are presented: pure and hybrid. These different versions are tested under different settings including two different GPU-equipped machines (a laptop and a server). The results show excellent improvement gains of the hybrid implementations compared with the pure parallel and sequential ones. On the laptop, the best gains of the hybrid implementations compared with the sequential ones are 113X for KM and 10.9X for FCM. As for the server, the best gains are 13.5X for KM and 16.3X for FCM. Moreover, the paper explores the usage of a recent memory management technique for GPU called Unified Memory (UM). The results show a decrease in the performance gain of the hybrid implementations that is equal to 44% for hybrid version of KM and 61% for FCM. On the other hand, the use of UM does introduce a small advantage for the pure parallel implementation.
In current scenario wireless sensor networks can be used in several areas like disaster management, intelligence surveillance and border security and so on. Wireless sensor networks (WSNs) are recognized as crucial an...
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ISBN:
(纸本)9781467394178
In current scenario wireless sensor networks can be used in several areas like disaster management, intelligence surveillance and border security and so on. Wireless sensor networks (WSNs) are recognized as crucial and critical way to facilitate computing environment in effective and efficient manner for numerous applications. The most considerable constraint in wireless sensor network is limited energy. Wireless sensor networks (WSNs) play vital role in computing environment due to its energy saving attributes. In such applications, a collection of sensor nodes are required which are unattended and work independently. clustering is a significant technique which is applied in sensor networks to achieve better utilization of nodes. It is also helpful to get better load distribution in network. In this paper, we survey and discuss various dimensions and approaches of some broadly discovered algorithms for clustering. This paper also presents a comparative study of various clustering algorithms and discussion about the potential research areas and the challenges of clustering in WSNs.
A powerful and flexible organization of documents can be obtained by mixing fuzzy and possibilistic clustering. In such organization, documents can belong to more than one cluster simultaneously with different compati...
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ISBN:
(纸本)9781509006274
A powerful and flexible organization of documents can be obtained by mixing fuzzy and possibilistic clustering. In such organization, documents can belong to more than one cluster simultaneously with different compatibility degrees. Clusters represent topics, which are identified by one or more descriptors extracted by a proposed method. In this manuscript, we investigated whether or not the descriptors extracted after applying possibilistic fuzzy clustering improve the flexible organization of documents. Experiments were carried out on real-world document collections and we evaluated the ability of descriptors to capture the essential information in every dataset. Results have shown the effectiveness of extracting possibilistic fuzzy cluster descriptors, improving the flexible organization of documents.
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