Automatic Histogram-based Fuzzy C-Means Clustering in MATLAB

模糊C均值聚类算法可有效解决遥感信息的不确定性和混合像元的划分。文中基于MATLAB平台，采用模糊C均值聚类对遥感影像进行分类，并运用混淆矩阵对分类结果进行了精度评定。实验结果表明，基于模糊C均值聚类使得分类后的图像很好地区分了地物类别，取得了较好效果。

将介绍在模式识别中使用K均值和C均值算法的MATLAB仿真。通过仿真，用户可以深入理解这两种算法的工作原理和应用。

K-means algorithm has long been a staple in machine learning and data mining fields, primarily for its effectiveness in clustering large-scale datasets. However, traditional K-means clustering doesn't inherently distinguish the varying discriminative power of features in data. To address this, the paper proposes an innovative clustering framework incorporating L2-norm regularization on feature weights, thereby enhancing clustering outcomes. This new approach builds on the Weighted K-means (W-K-means) algorithm by applying L2-norm regularization to feature weights, effectively balancing feature importance. For numerical datasets, this framework introduces the l2-Wkmeans algorithm, which uses conventional means as cluster centers. For categorical datasets, two variations—l2-NOF (Non-numeric features based on different smoothing modes) and l2-NDM (Non-numeric features based on distance metrics)—are proposed. The essence of these methods lies in their updated clustering objective function and derived update rules for cluster centers, membership matrices, and feature weights. Extensive experiments demonstrate the superior performance of the proposed algorithms on both numerical and categorical datasets. These methods exhibit advantages such as improved clustering accuracy, robustness to noisy data, and adaptability to high-dimensional data environments. This signifies that incorporating L2-norm regularization for feature weighting substantially enhances the clustering quality of K-means, especially for complex, high-dimensional datasets. Additionally, the study discusses the impact of regularization parameters on clustering performance, offering practical insights for tuning these parameters to optimize clustering results. This guidance allows users to select the appropriate regularization intensity based on task-specific and data-related characteristics. The research provides a fresh perspective on improving the K-means clustering algorithm by emphasizing feature importance through L2-norm regularization, enhancing both clustering power and generalizability. This method is valuable for large-scale datasets and scenarios that require nuanced feature differentiation, representing a significant step forward in clustering quality and advancing related research fields.

使用回归树算法和 ANFIS 训练生成模糊推理系统 (FIS)。

MATLAB development - variable width histogram. This tool allows for the creation of flexible histograms that adjust based on the underlying data distribution, enhancing data visualisation and analysis.

通过LEACH算法，将均匀分布在空间中的节点进行分簇，基于MATLAB平台。该算法通过选择簇头节点并将其余节点分配到相应的簇，以优化网络性能和延长网络寿命。

Normalized Histogram是MATLAB中的一种特殊柱状图，其面积总和为1。

该项目是对进入车库的汽车进行自动检查的初步阶段，目的是开发一种系统，能够自动识别车牌号，创建汽车数据库并存储汽车图像以进行进一步处理。该项目使用MATLAB开发，并采用统计方法和模板匹配方法来识别车牌字符。代码和模板进行了微调，以在受控环境中检测澳大利亚新南威尔士州的车牌文本。 系统硬件包括： Arduino微控制器 三个USB网络摄像头 超声波传感器 面包板 USB对接端口 跳线 LED模型车 如何运行： 将LED连接到Arduino引脚D11。 将超声波传感器的Trig引脚连接到D12，Echo连接到D13。 将摄像头模块连接到USB端口，以获取输入图像。 在MATLAB命令窗口中，输入webcamlist以检测连接的USB摄像头，并使用以下代码来测试摄像头： cam = webcam('Logitech'); preview(cam); 确保在运行前测试摄像头连接，并根据需要调整超声波传感器的设置。

AP聚类算法的源代码，基于MATLAB程序，有较详细解说。此代码实现了基于聚类的方法，通过图的结构和相似度计算，进行有效的数据分组。