A k-Median Algorithm with Running Time Independent of Data Size
نویسندگان
چکیده
منابع مشابه
A Fast Approximated k-Median Algorithm
The k-means algorithm is a well–known clustering method. Although this technique was initially defined for a vector representation of the data, the set median (the point belonging to a set P that minimizes the sum of distances to the rest of points in P ) can be used instead of the mean when this vectorial representation is not possible. The computational cost of the set median is O(|P |). Rece...
متن کاملThe size-independent oxygen cost of running.
PURPOSE The purpose of this investigation was to determine whether differences in running economy among children, adolescents, and adults can be explained by differences in resting metabolism, mass, and stature. METHODS Participants were 36 children, 23 adolescents, and 24 adults. Mass-specific gross oxygen cost per minute ([OV0312]O(2gross) x M-1), mass-specific gross oxygen cost per kilomet...
متن کاملPersistent K-Means: Stable Data Clustering Algorithm Based on K-Means Algorithm
Identifying clusters or clustering is an important aspect of data analysis. It is the task of grouping a set of objects in such a way those objects in the same group/cluster are more similar in some sense or another. It is a main task of exploratory data mining, and a common technique for statistical data analysis This paper proposed an improved version of K-Means algorithm, namely Persistent K...
متن کاملA Hybrid Data Clustering Algorithm Using Modified Krill Herd Algorithm and K-MEANS
Data clustering is the process of partitioning a set of data objects into meaning clusters or groups. Due to the vast usage of clustering algorithms in many fields, a lot of research is still going on to find the best and efficient clustering algorithm. K-means is simple and easy to implement, but it suffers from initialization of cluster center and hence trapped in local optimum. In this paper...
متن کاملOn Conditional Inactivity Time of Failed Components in an (n-k+1)-out-of-n System with Nonidentical Independent Components
In this paper, we study an (n-k+1)-out-of-n system by adopting their components to be statistically independent though nonidentically distributed. By assuming that at least m components at a fixed time have failed while the system is still working, we obtain the mixture representation of survival function for a quantity called the conditional inactivity time of failed components in the system. ...
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: Machine Learning
سال: 2004
ISSN: 0885-6125
DOI: 10.1023/b:mach.0000033115.78247.f0