J. Tayyebi; E. Hosseinzadeh
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 ...
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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 fuzzy data. Then, as an application, a method based on the proposed algorithm is presented to cluster incomplete fuzzy data. The method substitutes missing attribute by a trapezoidal fuzzy number to be determined by using the corresponding attribute of q nearest-neighbor. Comparisons and analysis of the experimental results demonstrate the capability of the proposed method.
H.6.3.1. Classifier design and evaluation
M. Moradi; J. Hamidzadeh
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 ...
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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 ratings to make robust predictions and suggesting qualified recommendations are two significant challenges in recommender systems. However, the latter is far from satisfactory because human decisions affected by environmental conditions and they might change over time. In this paper, we introduce an innovative method to impute ratings to missed components of the rating matrix. We also design an ensemble-based method to obtain Top-k recommendations. To evaluate the performance of the proposed method, several experiments have been conducted based on 10-fold cross validation over real-world data sets. Experimental results show that the proposed method is superior to the state-of-the-art competing methods regarding applied evaluation metrics.
