G K clustering approach to determine optimal number of clusters for Wireless Sensor Networks

Raghuvanshi, Ajay Singh; Tiwari, Sudarshan; Tripathi, Rajeev; Kishor, Nand

doi:10.1109/wcsn.2009.5434814

Cited by 6 publications

(4 citation statements)

References 18 publications

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“…The Davies Bouldin index defined by Kasturi J, Acharya R, Ramanathan M in 2003 and the Silhouetee index defined by Chen G, Jaradat S A, Banerjee N in 2002 are also two useful indexes [52] [53]. There are also Dunn's index defined by Bezdek J C, Pal N R in 1995, Krzanowski Lai index, Alternative Dunn index, Xie and Beni's index, Partition index, Separation index (Raghuvanshi A S, Tiwari S, Tripathi R, 2009) [54] [55]. Because some of the indexes have similar functions, the project selects several typical indexes among them.…”

Section: Evaluation Indexesmentioning

confidence: 99%

Pattern Classification of Stock Price Moving

Wang¹

2022

FCIS

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The stock is one of the most important instruments of finance. However, the tendency of stock always has a high level of irregularity. In stock market, the stock price moving is considered as a time series problem. Clustering method on stock data is one of the machine learning methods and it is one of the most important analysis methods of technical analysis. The aim of this project is to find an efficient unsupervised learning way to analysis the stock market data to make classification of the patterns on different stock price moving data and get useful information for investment decisions by implementing different clustering algorithms. For this aim, the research objective of this project is to compare several of clustering methods like K-means algorithm, EM algorithm, Canopy algorithm, specify the best number of clusters for each clustering method by several evaluation indexes, show the result of each clustering method and make evaluation on the results of these clustering methods on stock market data of standard S&P 500 stock marketing data. In addition, Weka 3 and Matlab are used to implement the clustering methods and evaluation program. Data visualization shows clearly that those public companies in the same cluster have similar stock price moving pattern. The experiment shows the result that K-means algorithm and EM algorithm perform effectively in stock price moving and Canopy algorithm can be used before K-means algorithm to improve the efficiency.

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Section: Evaluation Indexesmentioning

confidence: 99%

Pattern Classification of Stock Price Moving

Wang¹

2022

FCIS

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“…Finally, MHR paths are set according to the energy balance principle of the algorithm based on the residual energy, the load, and the number of nodes in the network. In [20], the authors use the Gustafson-Kessel algorithm, which optimizes the number of clusters in order to reduce the energy consumption based on the shape and the volume of clusters, the initial setup of a clustering algorithm, the distribution of the data objects, and the number of clusters. The authors of [21] showed that the optimal number of cluster heads (ONCH) algorithm over LEACH (the low-energy adaptive clustering hierarchy algorithm) is better than LEACH without ONCH as the energy consumption is reduced and the life of the sensor network is extended.…”

Section: Related Workmentioning

confidence: 99%

In-Network Data Aggregation for Ad Hoc Clustered Cognitive Radio Wireless Sensor Network

Mortada

Nasser

Mansour

et al. 2021

Sensors

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In cognitive radio wireless sensor networks (CRSN), the nodes act as secondary users. Therefore, they can access a channel whenever its primary user (PU) is absent. Thus, the nodes are assumed to be equipped with a spectrum sensing (SS) module to monitor the PU activity. In this manuscript, we focus on a clustered CRSN, where the cluster head (CH) performs SS, gathers the data, and sends it toward a central base station by adopting an ad hoc topology with in-network data aggregation (IDA) capability. In such networks, when the number of clusters increases, the consumed energy by the data transmission decreases, while the total consumed energy of SS increases, since more CHs need to perform SS before transmitting. The effect of IDA on CRSN performance is investigated in this manuscript. To select the best number of clusters, a study is derived aiming to extend the network lifespan, taking the SS requirements, the IDA effect, and the energy consumed by both SS and transmission into consideration. Furthermore, the collision rate between primary and secondary transmissions and the network latency are theoretically derived. Numerical results corroborate the efficiency of IDA to extend the network lifespan and minimize both the collision rate and the network latency.

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“…Raghuvanshi et al [50] have used an objective function based method to divide a data set into a set of clusters. In comparison to standard clustering, fuzzy clustering offer to assign a data point to more than one cluster, so that overlapping clusters can be handled confidently.…”

Section: Non-probabilistic Algorithms 413mentioning

confidence: 99%

A Review Study on Analytical Estimation of Optimal Number of Clusters in Wireless Sensor Networks

Kumar

Dhok

Tripathi

et al. 2014

TNC

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To improve energy efficiency, total network scalability and data aggregation in Wireless Sensor Networks (WSNs), sensor nodes are often grouped into disjoint and mostly non-overlapping subsets called clusters. To provide an energy-efficient system by clustering, there are three main challenges. First is to find the optimum number of nodes in a specific cluster, second is to find the optimum number of clusters in the network and the third is to find the optimum position of Cluster Head (CH) in a specific cluster. Selecting an optimum number of clusters in WSNs provide greater improvement in terms of system scalability, energy efficiency, collision reduction, network lifetime, latency, and efficient routing backbone in the network. Selection of optimal number of clusters in WSNs is affected by level at which WSNs is modeled viz. Radio Energy Model Level, Network Model Level and Clustering Level. The objective of this paper is to present a state-of-the-art survey of distinct analytical methods used to calculate the optimum number of clusters, and its time-line comparative analysis based on network type, mathematical formula for an optimal number of clusters, base station positioning, energy model, strengths, weaknesses and applications of WSNs. We have also discussed the impact of different parameters on selecting the optimal number of clusters in WSNs.

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G K clustering approach to determine optimal number of clusters for Wireless Sensor Networks

Cited by 6 publications

References 18 publications

Pattern Classification of Stock Price Moving

Pattern Classification of Stock Price Moving

In-Network Data Aggregation for Ad Hoc Clustered Cognitive Radio Wireless Sensor Network

A Review Study on Analytical Estimation of Optimal Number of Clusters in Wireless Sensor Networks

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