Table of Contents 1. Data and analysis objects 2. Purpose and analysis tasks 3. Methods and Tools 4. Data reading 5. Data understanding 6. Data preparation 7. Model training ?Edit 8. Model evaluation 9. Model tuning participates in prediction 1. Data and analysis objects txt file – “protein.txt”, mainly records 9 attributes of 25 countries. […]
Tag: k-means
Clustering algorithm–K-Means algorithm
cluster learning 1. Clustering concept: 1. Unsupervised problem: We have no labels in our hands, and the computer needs to learn the labels by itself. 2. Clustering: In a pile of data, similar things are grouped into a group, and a group is a category, also called a cluster. 3. Difficulty: How to evaluate the […]
[K-means clustering algorithm] Implementing iris clustering
Article directory Preface 1. Introduction to data sets 2. Usage steps 1. Guide package 1.2 Load the data set 1.3 Draw two-dimensional data distribution chart 1.4 Instantiate the K-means class and define the training function 1.5 Training 1.6 Visual display 2. Clustering algorithm 2.1.Visual generation 3 Other clustering algorithms for iris classification Foreword For example: […]
Matlab simulation of network data clustering algorithm based on improved K-means
Table of Contents 1. Program function description 2. Test software version and display of running results 3. Core program 4. Principle of this algorithm 5. Complete program 1. Program function description K-means is a basic partitioning method in cluster analysis, and the error sum of squares criterion function is often used as the clustering criterion. […]
K-means (K-means) algorithm
K-means (k-means, also recorded as kmeans) is one of the clustering algorithms. Due to its simple principle, strong interpretability, easy implementation, and fast convergence speed, it is widely used in data mining, cluster analysis, data clustering, It has a wide range of applications in the fields of pattern recognition, financial risk control, data science, intelligent […]
K-means clustering algorithm implements iris clustering
1.Experimental purpose: Understand the principle of K-means algorithm and use K-means algorithm to implement iris clustering. 2.K-meansIntroduction to the algorithm Clustering is a process of classifying and organizing data members that are similar in some aspects of the data set. Clustering is a technique for discovering this inherent structure. Clustering technology is often called unsupervised […]
(Experiment 4) Implementation of k-means algorithm
1. Load the data set from the file import numpy as np inf = 0x3f3f3f3f def loadDataSet(fileName): dataMat = [] fr = open(fileName) for line in fr.readlines(): curLine = line.strip().split(‘\t’) fltLine = list(map(float,curLine)) dataMat.append(fltLine) return dataMat 2. Calculate the Euclidean distance of two vectors def disEclud(vecA,vecB): return np.sqrt(np.sum(np.power(vecA-vecB,2))) 3. Construct a set containing K random […]
[Image segmentation based on Kmeans, Kmeans++ and binary K-means algorithm] Data Mining Experiment 3
Article directory I. Project task requirements II. Principle description KMeans KMeans++ Bipartite K-means Evaluation index-silhouette coefficient III. Dataset description IV. Specific implementation process V. Result analysis VI, complete code VII. Deep learning and image segmentation (supplement) CNN 1. Convolutional Layer: 2. Activation Function: 3. Pooling Layer: 4. Fully Connected Layer: working principle: Split effect I. […]
K-means algorithm and Python, C++ implementation
K-means is a commonly used clustering algorithm that aims to divide a data set into K different, non-overlapping subsets (clusters) such that each data point belongs to the center point of the nearest cluster represented by it. category. The specific steps of the algorithm are as follows: Initialization: Randomly select K data points as the […]
K-means clustering in OpenCV
Goals Learn to use the cv.kmeans() function in OpenCV for data clustering Understanding parameters Input parameters samples : Should be of np.float32 data type, each feature should be placed in one column. nclusters(K): The final number of clusters required criteria : This is the iteration termination criterion. When this criterion is met, the algorithm iteration […]