Sample Based Algorithm for k-Spatial Medians Clustering
نویسندگان
چکیده
منابع مشابه
Application of the k-spatial medians clustering
The most widely used partitioning method in cluster analysis is the k-means clustering which minimizes within-cluster sum of squares. However, the k-means clustering is sensitive to outliers or cluster structures. We introduce the k-spatial medians clustering which is less sensitive to outliers as an alternative to the k-means clustering and compare two clustering methods for some arti cial dat...
متن کاملAn Optimization K-Modes Clustering Algorithm with Elephant Herding Optimization Algorithm for Crime Clustering
The detection and prevention of crime, in the past few decades, required several years of research and analysis. However, today, thanks to smart systems based on data mining techniques, it is possible to detect and prevent crime in a considerably less time. Classification and clustering-based smart techniques can classify and cluster the crime-related samples. The most important factor in the c...
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 کاملParallel K-Medoids++ Spatial Clustering Algorithm Based on MapReduce
Clustering analysis has received considerable attention in spatial data mining for several years. With the rapid development of the geospatial information technologies, the size of spatial information data is growing exponentially which makes clustering massive spatial data a challenging task. In order to improve the efficiency of spatial clustering for large scale data, many researchers propos...
متن کاملA fast and recursive algorithm for clustering large datasets with k-medians
Clustering with fast algorithms large samples of high dimensional data is an important challenge in computational statistics. A new class of recursive stochastic gradient algorithms designed for the k-medians loss criterion is proposed. By their recursive nature, these algorithms are very fast and are well adapted to deal with large samples of data that are allowed to arrive sequentially. It is...
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: Korean Journal of Applied Statistics
سال: 2010
ISSN: 1225-066X
DOI: 10.5351/kjas.2010.23.2.367