Application of kernel k-means and kernel x-means clustering to obtain soil classes from cone penetration test data
نویسندگان
چکیده
منابع مشابه
Scalable Kernel Clustering: Approximate Kernel k-means
Kernel-based clustering algorithms have the ability to capture the non-linear structure in real world data. Among various kernel-based clustering algorithms, kernel k -means has gained popularity due to its simple iterative nature and ease of implementation. However, its run-time complexity and memory footprint increase quadratically in terms of the size of the data set, and hence, large data s...
متن کاملConsensus Kernel K-Means Clustering for Incomplete Multiview Data
Multiview clustering aims to improve clustering performance through optimal integration of information from multiple views. Though demonstrating promising performance in various applications, existing multiview clustering algorithms cannot effectively handle the view's incompleteness. Recently, one pioneering work was proposed that handled this issue by integrating multiview clustering and impu...
متن کاملLocalized Data Fusion for Kernel k-Means Clustering with Application to Cancer Biology
In many modern applications from, for example, bioinformatics and computer vision, samples have multiple feature representations coming from different data sources. Multiview learning algorithms try to exploit all these available information to obtain a better learner in such scenarios. In this paper, we propose a novel multiple kernel learning algorithm that extends kernel k-means clustering t...
متن کاملOn Potts Model Clustering, Kernel K-means, and Density Estimation
Many clustering methods, such as K-means, kernel K-means, and MNcut clustering, follow the same recipe: (1) choose a measure of similarity between observations; (ii) define a figure of merit assigning a large value to partitions of the data that put similar observations in the same cluster; (iii) optimize this figure of merit over partitions. Potts model clustering, introduced by Blatt, Wiseman...
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 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: Soils and Rocks
سال: 2020
ISSN: 1980-9743,1980-9743
DOI: 10.28927/sr.434607