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Comparison of K-Means and Fuzzy C-Means Algorithms on Different Cluster Structures

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      <string language="en">Comparison of K-Means and Fuzzy C-Means Algorithms on Different Cluster Structures</string>
      <string language="tr">K-Ortalamalar ve Bulanık C-Ortalamalar Algoritmalarının Farklı Küme Yapıları İçin Karşılaştırılması</string>
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      <string language="en">In this paper the K-means (KM) and the Fuzzy C-means (FCM) algorithms were compared for their computing performance and clustering accuracy on different shaped cluster structures which are regularly and irregularly scattered in two dimensional space. While the accuracy of the KM with single pass was lower than those of the FCM, the KM with multiple starts showed nearly the same clustering accuracy with the FCM. Moreover the KM with multiple starts was extremely superior to the FCM in computing time in all datasets analyzed. Therefore, when well separated cluster structures spreading with regular patterns do exist in datasets the KM with multiple starts was recommended for cluster analysis because of its comparable accuracy and runtime performances.
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      <string language="tr">Bu makalede K-Ortalamalar (KO) ve Bulanık C-Ortalamalar (BCO) algoritmalarının düzenli ve düzensiz olarak iki boyutlu uzayda dağılış gösteren farklı şekillerde küme yapılarında hesaplama performansı ve kümeleme geçerliliği açısından karşılaştırılması yapılmıştır. Tek geçişli KO'nun kümeleme geçerliliği BCO'dan daha düşük olmasına karşın çoklu geçişle KO'nun BCO'nunkiyle yaklaşık olarak aynı düzeyde bulunmuştur. Ayruca çok geçişli KO'nun analiz edilen tüm veri setlerinde BCO'dan çok fazla yüksek saptanmıştır. Bu nedenle, düzenli dağılış gösteren veri setlerinde BCO'ya göre kümeleme geçerliliği ve hesaplama performansı açısından çok geçişli KO'nun kullanılması önerilmiştir.</string>
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      <string language="tr">bulanık c-ortalamalar</string>
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      <string language="tr">yumuşak kümeleme</string>
      <string language="tr">sert kümeleme</string>
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