ارسل ملاحظاتك

ارسل ملاحظاتك لنا







Modex and Seed Detective: Two Novel Techniques for High Quality Clustering by Using Good Initial Seeds in K-Means

المصدر: مجلة جامعة الملك سعود - علوم الحاسب والمعلومات
الناشر: جامعة الملك سعود
المؤلف الرئيسي: Rahman, Md Anisur (Author)
مؤلفين آخرين: Islam, Md Zahidul (Co-Author) , Bossomaier, Terry (Co-Author)
المجلد/العدد: مج27, ع2
محكمة: نعم
الدولة: السعودية
التاريخ الميلادي: 2015
الصفحات: 113 - 128
DOI: 10.33948/0584-027-002-003
ISSN: 1319-1578
رقم MD: 973547
نوع المحتوى: بحوث ومقالات
اللغة: الإنجليزية
قواعد المعلومات: science
مواضيع:
كلمات المؤلف المفتاحية:
Clustering | Classification | K-Means | Cluster Evaluation | Data Mining
رابط المحتوى:
صورة الغلاف QR قانون
حفظ في:
LEADER 02307nam a22002537a 4500
001 1716422
024 |3 10.33948/0584-027-002-003 
041 |a eng 
044 |b السعودية 
100 |9 524951  |a Rahman, Md Anisur  |e Author 
245 |a Modex and Seed Detective:  |b Two Novel Techniques for High Quality Clustering by Using Good Initial Seeds in K-Means 
260 |b جامعة الملك سعود  |c 2015 
300 |a 113 - 128 
336 |a بحوث ومقالات  |b Article 
520 |b In this paper we present two clustering techniques called ModEx and Seed-Detective. ModEx is a modified version of an existing clustering technique called Ex-Detective. It addresses some limitations of Ex Detective. Seed-Detective is a combination of ModEx and Simple KMeans. Seed-Detective uses ModEx to produce a set of high quality initial seeds that are then given as input to K-Means for producing the final clusters. The high quality initial seeds are expected to produce high quality clusters through K-Means. The performances of Seed-Detective and ModEx are compared with the performances of Ex-Detective, PAM, Simple K-Means (SK), Basic Farthest Point Heuristic (BFPH) and New Farthest Point Heuristic (NFPH). We use three cluster evaluation criteria namely F-measure, Entropy and Purity and four natural datasets that we obtain from the UCI Machine learning repository. In the datasets our proposed techniques perform better than the existing techniques in terms of F-measure, Entropy and Purity. The sign test results suggest a statistical significance of the superiority of Seed-Detective (and ModEx) over the existing techniques 
653 |a تقنيات التعدين  |a الانتروبيا  |a الخوازرميات الجينية 
692 |b Clustering  |b Classification  |b K-Means  |b Cluster Evaluation  |b Data Mining 
700 |9 524952  |a Islam, Md Zahidul  |e Co-Author 
773 |c 003  |e Journal of King Saud University (Computer and Information Sciences)  |f Maǧalaẗ ǧamʼaẗ al-malīk Saud : ùlm al-ḥasib wa al-maʼlumat  |l 002  |m مج27, ع2  |o 0584  |s مجلة جامعة الملك سعود - علوم الحاسب والمعلومات  |v 027  |x 1319-1578 
700 |9 524953  |a Bossomaier, Terry  |e Co-Author 
856 |u 0584-027-002-003.pdf 
930 |d y  |p y  |q n 
995 |a science 
999 |c 973547  |d 973547 

عناصر مشابهة