دانلود مقاله انگلیسی رایگان:خوشه بندی داده های رابطه ای  چند منظوره بر اساس بهینه سازی ازدحام ذرات - 2019
بلافاصله پس از پرداخت دانلود کنید
دانلود مقاله انگلیسی سیستم های خبره رایگان
  • Clustering of multi-view relational data based on particle swarm optimization Clustering of multi-view relational data based on particle swarm optimization
    Clustering of multi-view relational data based on particle swarm optimization

    سال انتشار:

    2019


    عنوان انگلیسی مقاله:

    Clustering of multi-view relational data based on particle swarm optimization


    ترجمه فارسی عنوان مقاله:

    خوشه بندی داده های رابطه ای چند منظوره بر اساس بهینه سازی ازدحام ذرات


    منبع:

    Sciencedirect - Elsevier - Expert Systems With Applications, 123 (2019) 34-53: doi:10:1016/j:eswa:2018:12:053


    نویسنده:

    RenêPereira de Gusmão a , b , Francisco de A.T. de Carvalho a , ∗


    چکیده انگلیسی:

    Clustering of multi-view data has received increasing attention since it explores multiple views of data sets aiming at improving clustering accuracy. Particle Swarm Optimization (PSO) is a well-known population-based meta-heuristic successfully used in cluster analysis. This paper introduces two hybrid clustering methods for multi-view relational data. These hybrid methods combine PSO and hard clus- tering algorithms based on multiple dissimilarity matrices. These methods take advantage of the global convergence ability of PSO and the local exploitation of hard clustering algorithms in the position up- date step, aiming to improve the balance between exploitation and exploration processes. Moreover, the paper provides adapted versions of 11 fitness functions suitable for vector data aiming at dealing with multi-view relational data. Two performance criteria were used to evaluate the clustering quality using the two proposed methods over eleven real-world data sets including image and document data sets. Among new findings, it was observed that the top three fitness functions are Silhouette index, Xu index and Intra-cluster homogeneity. The performance of the proposed algorithms was compared with previ- ous single and multi-view relational clustering algorithms. The results show that the proposed methods significantly outperformed the other algorithms in the majority of cases. The results reinforce the im- portance of the application of techniques such as PSO-based clustering algorithms in the field of expert systems and machine learning. Such application enhances classification accuracy and cluster compactness. Besides, the proposed algorithms can be useful tools in content-based image retrieval systems, providing good categorizations and automatically learning relevance weights for each cluster of images and sets of views.
    Keywords: PSO | Cluster analysis | Multi-view clustering | Relational data


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 20
    حجم فایل: 1949 کیلوبایت

    قیمت: رایگان


    توضیحات اضافی:




اگر این مقاله را پسندیدید آن را در شبکه های اجتماعی به اشتراک بگذارید (برای به اشتراک گذاری بر روی ایکن های زیر کلیک کنید)

تعداد نظرات : 0

الزامی
الزامی
الزامی
rss مقالات ترجمه شده rss مقالات انگلیسی rss کتاب های انگلیسی rss مقالات آموزشی