A new method for selection optimum k value in k-NN classification algorithm

Maleki, Masoud; Eroğlu, Kübra; Aydemir, Önder; Manshoori, N.; Kayıkçıoğlu, Temel

doi:10.1109/siu.2013.6531324

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…The fundamental objective of K-NN algorithm is to find out the optimal classified features that are most similar to the test features and confirm the categories of the test features according to the number of feature categories [3,4,5]. The similarity between two features is measured by the Euclidean distance between them, where decreasing distance indicates increasing similarity.…”

Section: A K-nn Methodsmentioning

confidence: 99%

Advanced K-means clustering algorithm for large ECG data sets based on K-SVD approach

Balouchestani

Sugavaneswaran

Krishnan

2014

2014 9th International Symposium on Communication Systems, Networks &Amp; Digital Sign (CSNDSP)

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Wireless electrocardiography (ECG) systems are crucial in detecting and diagnosing heart disorders. Minimizing power consumption and sampling-rate should be the key aspects when designing wireless ECG systems. In order to achieve portability coupled with ultra-low power consumption and sampling-rate, clustering and classification algorithms play an important role in developing wireless ECG systems. Currently used algorithms do have their share of drawbacks: 1) clustering and classification cannot be done in real time; 2) implementing existing algorithms would lead to higher computational costs. These drawbacks motivated us in developing novel optimized clustering algorithm which could easily scan large ECG datasets for characteristic bio-markers. In this paper, we present an advanced K-means clustering algorithm based on K-Singular Value Decomposition (K-SVD) approach with a connection to Compressed Sensing (CS) theory, followed by sorting the data using a K-Nearest Neighbours (K-NN) classifier. The proposed algorithm outperforms existing algorithms by achieving a classification accuracy of 99.3%. This ability allows reducing 15% of Average Classification Error (ACE). The proposed algorithm also reduces the clustering energy consumption by increasing the classification performance.

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