A Fast Multiobjective Fuzzy Clustering with Multimeasures Combination
المؤلفون المشاركون
Chen, Yingxia
Liu, Cong
Chen, Qianqian
Liu, Jie
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2019، العدد 2019 (31 ديسمبر/كانون الأول 2019)، ص ص. 1-21، 21ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2019-01-17
دولة النشر
مصر
عدد الصفحات
21
التخصصات الرئيسية
الملخص EN
Most of the existing clustering algorithms are often based on Euclidean distance measure.
However, only using Euclidean distance measure may not be sufficient enough to partition a dataset with different structures.
Thus, it is necessary to combine multiple distance measures into clustering.
However, the weights for different distance measures are hard to set.
Accordingly, it appears natural to keep multiple distance measures separately and to optimize them simultaneously by applying a multiobjective optimization technique.
Recently a new clustering algorithm called ‘multiobjective evolutionary clustering based on combining multiple distance measures’ (MOECDM) was proposed to integrate Euclidean and Path distance measures together for partitioning the dataset with different structures.
However, it is time-consuming due to the large-sized genes.
This paper proposes a fast multiobjective fuzzy clustering algorithm for partitioning the dataset with different structures.
In this algorithm, a real encoding scheme is adopted to represent the individual.
Two fuzzy clustering objective functions are designed based on Euclidean and Path distance measures, respectively, to evaluate the goodness of each individual.
An improved evolutionary operator is also introduced accordingly to increase the convergence speed and the diversity of the population.
In the final generation, a set of nondominated solutions can be obtained.
The best solution and the best distance measure are selected by using a semisupervised method.
Afterwards, an updated algorithm is also designed to detect the optimal cluster number automatically.
The proposed algorithms are applied to many datasets with different structures, and the results of eight artificial and six real-life datasets are shown in experiments.
Experimental results have shown that the proposed algorithms can not only successfully partition the dataset with different structures, but also reduce the computational cost.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Liu, Cong& Chen, Qianqian& Chen, Yingxia& Liu, Jie. 2019. A Fast Multiobjective Fuzzy Clustering with Multimeasures Combination. Mathematical Problems in Engineering،Vol. 2019, no. 2019, pp.1-21.
https://search.emarefa.net/detail/BIM-1195298
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Liu, Cong…[et al.]. A Fast Multiobjective Fuzzy Clustering with Multimeasures Combination. Mathematical Problems in Engineering No. 2019 (2019), pp.1-21.
https://search.emarefa.net/detail/BIM-1195298
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Liu, Cong& Chen, Qianqian& Chen, Yingxia& Liu, Jie. A Fast Multiobjective Fuzzy Clustering with Multimeasures Combination. Mathematical Problems in Engineering. 2019. Vol. 2019, no. 2019, pp.1-21.
https://search.emarefa.net/detail/BIM-1195298
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1195298
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