Clustering Multi-Attribute Uncertain Data using Probability Distribution
نویسندگان
چکیده
منابع مشابه
Clustering Multi-Attribute Uncertain Data using Probability Distribution
Clustering is an unsupervised classification technique for grouping set of abstract objects into classes of similar objects. Clustering uncertain data is one of the essential tasks in mining uncertain data. Uncertain data is typically found in the area of sensor networks, weather data, customer rating data etc. The earlier methods for clustering uncertain data based on probability distribution,...
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Today we have seen so much digital uncertain data produced. Handling of this uncertain data is very difficult. Commonly, the distance between these uncertain object descriptions are expressed by one numerical distance value. Clustering on uncertain data is one of the essential and challenging tasks in mining uncertain data. The previous methods extend partitioning clustering methods like k-mean...
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Cluster analysis is one of the important data analysis methods and is a very complex task. It is the art of a detecting group of similar objects in large data sets without requiring specified groups by means of explicit features or knowledge of data. Clustering on uncertain data is a most difficult task in both modeling similarity between uncertain data objects and developing efficient computat...
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ژورنال
عنوان ژورنال: International Journal of Computer Applications
سال: 2014
ISSN: 0975-8887
DOI: 10.5120/17812-8641