A Novel Artificial Bee Colony Based Clustering Algorithm for Categorical Data

Ji, Jinchao; Pang, Wei; Zheng, Yanlin; Wang, Zhe; Ma, Zhiqiang

doi:10.1371/journal.pone.0127125

Cited by 23 publications

(15 citation statements)

References 32 publications

(31 reference statements)

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“…network performance lead to matching the sender's data with the recipient's data, reduce end to end delay and increase the throughput. For that purpose, WBAN needs to enhance network performance by reducing the number packet loss, end to end delay and increase the throughput by providing an efficient communication scheme in WBAN [8]. packet loss, end to end delay and low throughput leads to incomplete data access at the monitoring devices (a doctor's phone or care center), risking the lives of the patient as the dosage of drugs or procedures depend on this data, especially comatose patients [9].…”

Section: Problem Statementmentioning

confidence: 99%

“…It was necessary to choose an efficient method that included an organized and approved method in previous studies for the same purpose. This study adopted the Artificial Bee Colony clustering based on K-Modes (ABC-K-Modes) [8] to collect the vital data of sensors because this algorithm uses artificial method in collecting data [9,10]. The algorithm assumes that two sets of groups (cluster1 and cluster2) distributed sensors according to the formula of distance, which is applied randomly to any two pair sensors.…”

Section: A Collecting Datamentioning

confidence: 99%

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Identification key scheme to enhance network performance in wireless body area network

Albaeazanchi

Abdulshaheed

Shawkat‎‎³

et al. 2019

PEN

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Wireless Body Area Network (WBAN) refers to a group of small intelligent electronic devices placed on the human body to monitor its vital signals. the efficiency of network performance in health care applications is one of the most important prerequisites for designing an effective surveillance system. However, none of the previous studies have succeeded in proposing an effective scheme that could cover the requirements for effective network performance or the development of the WBAN system capable of sending and receiving vital patient data efficiently. The aim of this paper is to enhance the performance of the network in WBAN. In addition, to verify the transfer of patient data from sensors on the body to receivers with the least value of possible packet loss, a solution is developed that can reduce the value of packet loss, end to end delay and increase the value of throughput, relying on confirmation and matching data between the sender and receiver devices. A new scheme known as the Identification Key Scheme (IKS) is proposed based on the newly available 402-405 MHz Medical Implant Communication Service (MICS). The analysis of the network performance was implemented in real WBAN dataset and used the Multi-Parameter Intelligent Monitoring for Intensive Care (MIMIC) database. MIMIC database included the data recorded from 90 Intensive Care Unit (ICU) patients. In this study, OMNET++ was used as the network simulation tool to design and evaluate the proposed scheme. The results showed that IKS was able to reduce number of packet loss and end to end delay with increased throughput. This is an indicator of the ability of IKS in providing efficient network performance between devices in the WBAN application.

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Section: Problem Statementmentioning

confidence: 99%

Section: A Collecting Datamentioning

confidence: 99%

Identification key scheme to enhance network performance in wireless body area network

Albaeazanchi

Abdulshaheed

Shawkat‎‎³

et al. 2019

PEN

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“…The modified algorithm is able to enhance the quality of clustering in addition to reducing the latency. J i et al [184] introduced ABC-K-Modes clustering algorithm for categorical data by use of traditional k-modes method. One-step k-modes procedure was used for solution search in employed and onlooker phase.…”

Section: Abc Applications In Data Clusteringmentioning

confidence: 99%

A Review on Artificial Bee Colony Algorithms and Their Applications to Data Clustering

Kumar

Jarial

2017

Cybernetics and Information Technologies

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“…Artificial Bee Colony (ABC) is combined with k-modes is proposed in [15]. In this paper, one step k-modes clustering algorithm is executed and then integrate this procedure with the artificial bee colony approach.…”

Section: Related Workmentioning

confidence: 99%

CLUSTERING CATEGORICAL DATA USING k-MODES BASED ON CUCKOO SEARCH OPTIMIZATION ALGORITHM

Lakshmi¹,

Visalakshi²,

Shanthi³

et al. 2017

IJSC

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Cluster analysis is the unsupervised learning technique that finds the interesting patterns in the data objects without knowing class labels. Most of the real world dataset consists of categorical data. For example, social media analysis may have the categorical data like the gender as male or female. The k-modes clustering algorithm is the most widely used to group the categorical data, because it is easy to implement and efficient to handle the large amount of data. However, due to its random selection of initial centroids, it provides the local optimum solution. There are number of optimization algorithms are developed to obtain global optimum solution. Cuckoo Search algorithm is the population based metaheuristic optimization algorithms to provide the global optimum solution. Methods: In this paper, k-modes clustering algorithm is combined with Cuckoo Search algorithm to obtain the global optimum solution. Results: Experiments are conducted with benchmark datasets and the results are compared with k-modes and Particle Swarm Optimization with k-modes to prove the efficiency of the proposed algorithm.

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A Novel Artificial Bee Colony Based Clustering Algorithm for Categorical Data

Cited by 23 publications

References 32 publications

Identification key scheme to enhance network performance in wireless body area network

Identification key scheme to enhance network performance in wireless body area network

A Review on Artificial Bee Colony Algorithms and Their Applications to Data Clustering

CLUSTERING CATEGORICAL DATA USING k-MODES BASED ON CUCKOO SEARCH OPTIMIZATION ALGORITHM

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