1. A Fuzzy C-means Algorithm for Clustering Fuzzy Data and Its Application in Clustering Incomplete Data

J. Tayyebi; E. Hosseinzadeh

Volume 8, Issue 4 , Autumn 2020, , Pages 515-523

http://dx.doi.org/10.22044/jadm.2020.9021.2038

Abstract
  The fuzzy c-means clustering algorithm is a useful tool for clustering; but it is convenient only for crisp complete data. In this article, an enhancement of the algorithm is proposed which is suitable for clustering trapezoidal fuzzy data. A linear ranking function is used to define a distance for trapezoidal ...  Read More

H.6.3.1. Classifier design and evaluation
2. Ensemble-based Top-k Recommender System Considering Incomplete Data

M. Moradi; J. Hamidzadeh

Volume 7, Issue 3 , Summer 2019, , Pages 393-402

http://dx.doi.org/10.22044/jadm.2019.7026.1830

Abstract
  Recommender systems have been widely used in e-commerce applications. They are a subclass of information filtering system, used to either predict whether a user will prefer an item (prediction problem) or identify a set of k items that will be user-interest (Top-k recommendation problem). Demanding sufficient ...  Read More