Unsupervised clustering using K-means algorithm

K-means clustering is a commonly used unsupervised clustering algorithm that implements clustering by dividing the data set into k clusters, each cluster containing similar data points. High similarity within clusters and low similarity between clusters. This article will introduce how to use K-means for unsupervised clustering.

1. The basic principles of K-means clustering

K-means clustering is a commonly used unsupervised learning algorithm, and its basic principles It is to divide the data points into k clusters, so that each data point belongs to one of the clusters, and the similarity of the data points within the cluster is as high as possible, and the similarity between different clusters is as low as possible. The specific steps are as follows:

1. Initialization: randomly select k data points as cluster centers.

2. Assignment: Assign each data point to the cluster where its nearest cluster center is located.

3. Update: Recalculate the cluster center of each cluster.

4. Repeat steps 2 and 3 until the clusters no longer change or the predetermined number of iterations is reached.

The goal of K-means clustering is to minimize the sum of the distances between the data points in each cluster and the cluster center. This distance is also called the "intra-cluster sum of squares error ( SSE)". The algorithm stops iterating when the SSE value no longer decreases or reaches a predetermined number of iterations.

2. Implementation steps of K-means clustering

The implementation steps of K-means clustering algorithm are as follows:

1. Select k clustering centers: Randomly select k data points from the data set as clustering centers.

2. Calculate distance: Calculate the distance between each data point and k cluster centers, and select the cluster with the closest cluster center.

3. Update the cluster center: Recalculate the cluster center for each cluster, that is, the average coordinate of all data points in the cluster is used as the new cluster center.

4. Repeat steps 2 and 3 until the predetermined number of iterations is reached or the clusters no longer change.

5. Output clustering results: Assign each data point in the data set to the final cluster and output the clustering results.

When implementing the K-means clustering algorithm, you need to pay attention to the following points:

1. Initialization of the cluster center: Cluster center The choice of has a great impact on the clustering effect. Generally speaking, k data points can be randomly selected as cluster centers.

2. Selection of distance calculation methods: Commonly used distance calculation methods include Euclidean distance, Manhattan distance and cosine similarity. Different distance calculation methods are suitable for different types of data.

3. Selection of the number of clusters k: The selection of the number of clusters k is often a subjective issue and needs to be selected according to the specific application scenario. Generally speaking, the optimal number of clusters can be determined through methods such as the elbow method and silhouette coefficient.

3. Advantages and disadvantages of K-means clustering

The advantages of K-means clustering include:

1. Simple to understand and easy to implement.

2. Can handle large-scale data sets.

3. When the data distribution is relatively uniform, the clustering effect is better.

The disadvantages of K-means clustering include:

1. It is sensitive to the initialization of the cluster center and may converge to the local optimum. untie.

2. The processing of abnormal points is not effective enough.

3. When the data distribution is uneven or there is noise, the clustering effect may be poor.

4. Improved methods of K-means clustering

In order to overcome the limitations of K-means clustering, researchers have proposed many improvements Methods, including:

1.K-Medoids clustering: changing the cluster center from a data point to a representative point (medoid) within the cluster can better handle outliers and noise.

2. Density-based clustering algorithms: such as DBSCAN, OPTICS, etc., can better handle clusters of different densities.

3. Spectral clustering: treat data points as nodes in the graph, regard similarity as edge weights, implement clustering through spectral decomposition of the graph, and can handle non-convex of clusters and clusters of different shapes.

4. Hierarchical clustering: Treat data points as nodes in the tree, and implement clustering by continuously merging or splitting clusters to obtain the hierarchical structure of the clusters.

5. Fuzzy clustering: Allocate data points to different clusters. Each data point has a membership degree for each cluster, which can handle the uncertainty of data points. big situation.

In short, K-means clustering is a simple and effective unsupervised clustering algorithm, but in practical applications, we need to pay attention to its limitations and can be combined with other improvement methods to improve clustering. class effect.

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