Data Distribution Aware Classification Algorithm based on K-Means

Tulgar, Tamer; Haydar, Ali; Erşan, İbrahim

doi:10.14569/ijacsa.2017.080946

Cited by 4 publications

(6 citation statements)

References 12 publications

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“…The only dataset where the proposed Z-KNN algorithm is not performing better than the competitors is the Wine dataset. From the results, which were observed in [17], it can be deduced that the low performance of the Z-KNN can be explained by the data distribution features of the Wine dataset that can be remedied by introducing the variance effect contribution to the similarity analysis.…”

Section: The Resultsmentioning

confidence: 99%

“…On the other hand, especially when a data set with high number of instances and high number of features per instance needs to be classified, the classical K-NN algorithm's classification accuracy performance becomes lower than its other well-known competitors, like K-Means classification [17].…”

Section: A the Classical K-nn Algorithmmentioning

confidence: 99%

“…As the future works, it is planned to introduce the effect of the variance to the distance calculation, from the study proposed in [17]. It is expected that, especially the weakness that can be seen in the wine data set can be significantly improved when variance effect is introduced to the distance calculations.…”

Section: The Conclusion and The Future Workmentioning

confidence: 99%

See 2 more Smart Citations

A Distributed K Nearest Neighbor Classifier for Big Data

Tulgar¹,

Haydar²,

Erşan³

2018

Balkan Journal of Electrical and Computer Engineering

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The K-Nearest Neighbor classifier is a well-known and widely applied method in data mining applications. Nevertheless, its high computation and memory usage cost makes the classical K-NN not feasible for today's Big Data analysis applications. To overcome the cost drawbacks of the known data mining methods, several distributed environment alternatives have emerged. Among these alternatives, Hadoop MapReduce distributed ecosystem attracted significant attention. Recently, several K-NN based classification algorithms have been proposed which are distributed methods tested in Hadoop environment and suitable for emerging data analysis needs. In this work, a new distributed Z-KNN algorithm is proposed, which improves the classification accuracy performance of the well-known K-Nearest Neighbor (K-NN) algorithm by benefiting from the representativeness relationship of the instances belonging to different data classes. The proposed algorithm relies on the data class representations derived from the Z data instances from each class, which are the closest to the test instance. The Z-KNN algorithm was tested in a physical Hadoop Cluster using several real-datasets belonging to different application areas. The performance results acquired after extensive experiments are presented in this paper and they prove that the proposed Z-KNN algorithm is a competitive alternative to other studies recently proposed in the literature

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Section: The Resultsmentioning

confidence: 99%

Section: A the Classical K-nn Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

A Distributed K Nearest Neighbor Classifier for Big Data

Tulgar¹,

Haydar²,

Erşan³

2018

Balkan Journal of Electrical and Computer Engineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…In [5] proposes a better K-Means algorithm to improve the classification precision, when K-Means cannot adequately classify data under certain data distribution conditions. The proposal considers the effect of variance on the classification so that the data can be classified with greater exactness.…”

Section: Related Workmentioning

confidence: 99%

“…The K-means machine learning algorithm [18] is used to group a known, assumed, or indicated in advance dataset. In [5] K-means is a classic prototype-based partitioning grouping technique that attempts to group data into k groupings that have been specified by the user.…”

Section: A K-means Algorithmmentioning

confidence: 99%

Classification Model of Municipal Management in Local Governments of Peru based on K-means Clustering Algorithms

Morales¹,

Vargas²,

Coyla³

et al. 2020

IJACSA

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The K-means algorithm groups datasets into different groups, defines a fixed number of clusters, iteratively assigning data to the clusters formed by adjusting the centers in each cluster. K-means algorithm uses an unsupervised learning method to discover patterns in an input data set. The purpose of the research is to propose a municipal management classification model in the municipalities of Peru using a K-means clustering algorithm based in 58 variables obtained from the areas of human resources, heavy machinery and operating vehicles, information and communication technologies, municipal planning, municipal finances, local economic development, social services, solid waste management, cultural, recreational and sports facilities, public security, disaster risk management, environmental protection and conservation of all the municipalities of the 24 departments of Peru and the constitutional province of Callao. The results of the application of the K-means algorithm show that 32% of the municipalities made up of the municipal governments of Amazonas,

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Intelligent recommendation method of an exercise program based on physical health data of college students

Zhang

Miao

2023

Applied Artificial Intelligence

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Data Distribution Aware Classification Algorithm based on K-Means

Cited by 4 publications

References 12 publications

A Distributed K Nearest Neighbor Classifier for Big Data

A Distributed K Nearest Neighbor Classifier for Big Data

Classification Model of Municipal Management in Local Governments of Peru based on K-means Clustering Algorithms

Intelligent recommendation method of an exercise program based on physical health data of college students

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