*K-Means and Cluster Models for Cancer Signatures
نویسندگان
چکیده
منابع مشابه
*K-means and cluster models for cancer signatures
We present *K-means clustering algorithm and source code by expanding statistical clustering methods applied in https://ssrn.com/abstract=2802753 to quantitative finance. *K-means is statistically deterministic without specifying initial centers, etc. We apply *K-means to extracting cancer signatures from genome data without using nonnegative matrix factorization (NMF). *K-means' computational ...
متن کامل3D Building Models Segmentation Based on K-means++ Cluster Analysis
3D mesh model segmentation is drawing increasing attentions from digital geometry processing field in recent years. The original 3D mesh model need to be divided into separate meaningful parts or surface patches based on certain standards to support reconstruction, compressing, texture mapping, model retrieval and etc. Therefore, segmentation is a key problem for 3D mesh model segmentation. In ...
متن کاملK-Means Cluster Analysis for Image Segmentation
Does K-Means reasonably divides the data into k groups is an important question that arises when one works on Image Segmentation? Which color space one should choose and how to ascertain that the k we determine is valid? The purpose of this study was to explore the answers to aforementioned questions. We perform K-Means on a number of 2-cluster, 3cluster and k-cluster color images (k>3) in RGB ...
متن کاملFaster K-Means Cluster Estimation
K-means is a widely used iterative clustering algorithm. There has been considerable work on improving k-means in terms of mean squared error (MSE) and speed, both. However, most of the k-means variants tend to compute distance of each data point to each cluster centroid for every iteration. We propose two heuristics to overcome this bottleneck and speed up k-means. Our first heuristic predicts...
متن کاملRanking and Clustering Iranian Provinces Based on COVID-19 Spread: K-Means Cluster Analysis
Introduction: The Coronavirus has crossed geographical borders. This study was performed to rank and cluster Iranian provinces based on coronavirus disease (COVID-19) recorded cases from February 19 to March 22, 2020. Materials and Methods: This cross-sectional study was conducted in 31 provinces of Iran using the daily number of confirmed cases. Cumulative Frequency (CF) and Adjusted CF (ACF)...
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: SSRN Electronic Journal
سال: 2017
ISSN: 1556-5068
DOI: 10.2139/ssrn.2908286