Tag:clustering
-
K-means clustering of advertising effect
Project background Explore classic data sets in depth. 1 data set review import matplotlib. Pyplot as plot # graphics library import numpy as np import pandas as pd from sklearn. metrics import silhouette_ Score # import contour coefficient index from sklearn. Cluster import kmeans # kmeans module from sklearn. Preprocessing import minmaxscaler, onehotencoder # data […]
-
22 machine learning open basic course — principal component analysis and clustering
Principal component analysis and clustering Unsupervised learning is one of the most important machine learning algorithms. Compared with supervised learning algorithm, unsupervised learning algorithm does not need to mark the input data, that is, it does not need to give labels or categories. In addition, unsupervised learning algorithm can also learn the internal relationship of […]
-
RVN: a new clustering algorithm
When we need to cluster data sets, the first algorithms we may study are k means, DBSCAN and hierarchical clustering. Those classical clustering algorithms always regard each data point as a point. However, these data points usually have size or boundary (bounding box) in real life. Ignoring the edge of the point may lead to […]
-
What are the clustering algorithms? How are they classified?
If you want to understand the clustering algorithm and distinguish and compare it, you’d better understand the specific clustering algorithm in the context of the whole clustering analysis. Cluster analysis is a more rigorous methodData analysisProcess. There are four main research contents from the data source of clustering object to the knowledge archive of clustering […]
-
Clustering of sklearn learning
1. Algorithm Introduction 1. Cognitive clustering algorithm Classify sharks, sheep, cats, dogs, frogs, goldfish and sparrows According to the way of reproduction: Shark sheep cat dog Frog goldfish sparrow According to the way of breathing Sheep cat dog frog sparrow Goldfish shark According to living environment Sheep cat dog sparrow Goldfish shark frogUsing different clustering […]
-
Quantitative Ecology: application of R language Chapter 4 cluster analysis 3 – non hierarchical clustering
Quantitative Ecology: application of R language Chapter 4 cluster analysis 3 – non hierarchical clustering Hierarchical clustering is often used in cluster analysis. Hierarchical clustering calculates the similarity between nodes through a certain similarity measure, sorts them from high to low, and gradually reconnects nodes. Another kind of non hierarchical clustering is a simple grouping […]
-
A simple understanding of NMF data typing
brief introduction NMF, also known as non negative matrix decomposition, can be understood as another dimension reduction clustering method, which is often used in single cell clustering, cancer RNA SEQ data clustering and other applications. The following figure is the schematic diagram of NMF decomposition: Where: V matrix is an n × Matrix of M, […]
-
Machine learning – kmeans algorithm principle & & spark implementation
The data developer who doesn’t understand the algorithm is not a good algorithm engineer. I still remember some data mining algorithms mentioned by my tutor when I was a graduate student. I was interested, but I had little contact after working. The Data Engineer despised the chain. Model > real-time > offline warehouse > ETL […]
-
R language uses self-organizing mapping neural network (SOM) to segment customers
Original link:http://tecdat.cn/?p=18726 Original source:Tuo end data tribal official account _ Self organization_ Mapped neural network (SOM) is an unsupervised data visualization technology, which can be used to visualize high-dimensional data sets in low-dimensional (usually 2-dimensional) representation. In this article, we studied how to use r to create SOM for customer segmentation. SOM was first described […]
-
WGCNA graphic explanation of IMPS (VI) – visualize a weighted network
It’s still the old habit to give a tutorial on the official website. As for whether you look or not, it’s right there waiting for your in-depth study ~ https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/Rpackages/WGCNA/Tutorials/ WGCNA analyzes the fifth figure to be explained in the topic of detailed explanation of pictures and texts, and the visualization of weighted co expression […]
-
Recommended collection! Complete operation examples of 10 Python clustering algorithms
Clustering analysis is unsupervised or unsupervised. It is often used as a data analysis technique to discover interesting patterns in data, such as customer groups based on their behavior. There are many clustering algorithms to choose from. For all cases, there is no single optimal clustering algorithm. Instead, it’s best to explore a series of […]
-
[mindspire: let’s learn machine with little Mi!] clustering algorithm
I haven’t seen you for a week. I miss you very much! Today, little Mi takes you to learn clustering algorithm! That is, we have finished learning the mainstream supervised learning algorithms. In this issue, we began to contact unsupervised learning algorithms. No more nonsense. Start learning with little MI~ 1. Unsupervised learningWhat is unsupervised […]