Sørensen-dice similarity indexing based weighted iterative clustering for big data analytics
المؤلفون المشاركون
Annathurai, Kalyana Saravanan
Angamuthu, Tamilarasi
المصدر
The International Arab Journal of Information Technology
العدد
المجلد 19، العدد 1 (31 يناير/كانون الثاني 2022)، ص ص. 11-22، 12ص.
الناشر
جامعة الزرقاء عمادة البحث العلمي
تاريخ النشر
2022-01-31
دولة النشر
الأردن
عدد الصفحات
12
التخصصات الرئيسية
تكنولوجيا المعلومات وعلم الحاسوب
الملخص EN
Big data is a collection of large volume of data and extract similar data points from large dataset.
Clustering is an essential data mining technique for examining large volume of data.
Several techniques have been developed for handling big dataset.
However, with much time consumption and space complexity, accuracy is said to be compromised.
In order to improve clustering accuracy with less complexity, Sørensen-Dice Indexing based Weighted Iterative X-means Clustering (SDI-WIXC) technique is introduced.
SDI-WIXC technique is used for grouping the similar data points with higher clustering accuracy and minimal time.
First, number of data points is collected from big dataset.
Then, along with the weight value, the given dataset is partitioned into ‘X’ number of clusters.
Next, based on the similarity measure, Weighted Iterated X-means Clustering (WIXC) is applied for clustering data points.
Sørensen-Dice Indexing Process is used for measuring similarity between cluster weight value and data points.
Upon similarity found between weight value of cluster and data point, data points are grouped into a specific cluster.
Besides, the WIXC method also improves the cluster assignments through repeated subdivision using Bayesian probability criterion.
This in turn helps to group all data points and hence, improving the clustering accuracy.
Experimental evaluation is carried out with number of factors such as clustering accuracy, clustering time and space complexity with respect to the number of data points.
The experimental results reported that the proposed SDI-WIXC technique obtains high clustering accuracy with minimum time as well as space complexity.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Annathurai, Kalyana Saravanan& Angamuthu, Tamilarasi. 2022. Sørensen-dice similarity indexing based weighted iterative clustering for big data analytics. The International Arab Journal of Information Technology،Vol. 19, no. 1, pp.11-22.
https://search.emarefa.net/detail/BIM-1437410
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Annathurai, Kalyana Saravanan& Angamuthu, Tamilarasi. Sørensen-dice similarity indexing based weighted iterative clustering for big data analytics. The International Arab Journal of Information Technology Vol. 19, no. 1 (Jan. 2022), pp.11-22.
https://search.emarefa.net/detail/BIM-1437410
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Annathurai, Kalyana Saravanan& Angamuthu, Tamilarasi. Sørensen-dice similarity indexing based weighted iterative clustering for big data analytics. The International Arab Journal of Information Technology. 2022. Vol. 19, no. 1, pp.11-22.
https://search.emarefa.net/detail/BIM-1437410
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references : p. 20-21
رقم السجل
BIM-1437410
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر