Three-Way Ensemble Clustering for Incomplete Data
نویسندگان
چکیده
منابع مشابه
A Fuzzy C-means Algorithm for Clustering Fuzzy Data and Its Application in Clustering Incomplete Data
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 pres...
متن کاملEnsemble-based Top-k Recommender System Considering Incomplete Data
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 si...
متن کاملA hierarchical mixture model for clustering three-way data sets
Three-way data sets occur when various attributes aremeasured for a set of observational units in different situations. Examples are genotype by environment by attribute data obtained in a plant experiment, individual by time point by response data in a longitudinal study, and individual by brand by attribute data in a market research survey. Clustering observational units (genotypes/individual...
متن کاملModel based clustering for three-way data structures
The technological progress of the last decades has made a huge amount of information available, often expressed in unconventional formats. Among these, three-way data occur in different application domains from the simultaneous observation of various attributes on a set of units in different situations or locations. These include data coming from longitudinal studies of multiple responses, spat...
متن کاملWeighted Ensemble Clustering for Increasing the Accuracy of the Final Clustering
Clustering algorithms are highly dependent on different factors such as the number of clusters, the specific clustering algorithm, and the used distance measure. Inspired from ensemble classification, one approach to reduce the effect of these factors on the final clustering is ensemble clustering. Since weighting the base classifiers has been a successful idea in ensemble classification, in th...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Access
سال: 2020
ISSN: 2169-3536
DOI: 10.1109/access.2020.2994380