LMS111

K-means Fuzzy Clustering file

用MATLAB进行探索数据分析，理论和应用同时介绍，并在书中列出不少MATLAB实现的例子(Exploratory Data Analysis with MATLAB, introduce exploratory data analysis methods and algorithms)

清华大学周东华教授提出的强跟踪滤波器，有效改善突发干扰下的跟踪能力。(Strong tracking filter supposed by Pro. ZHOU Dong-hua, which enhanced the tracking ability during sudden disturb.)

the graphs where you can find how the function looks like - sinus

Artificial Neural Networks Exercise in Matlab

本程序主要是对线性判别函数分类器进行实现。(Linear discriminant function classifier achieved.)

Continuous Ant Colony Optimization

宝钢厚板厂物流分流技术，分流清晰，有仿真，可以借鉴。(park transfer model，universial)

How the K-mean Cluster work Step 1. Begin with a decision the value of k = number of clusters Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following: Take the first k training sample as single-element clusters Assign each of the remaining (N-k) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster. Step 3 . Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample. Step 4 . Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments. (How the K-mean Cluster workStep 1. Begin with a decision the value of k = number of clusters Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following: Take the first k training sample as single-element clusters Assign each of the remaining (Nk) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster. Step 3. Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample. Step 4. Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments.)

k mean clustering in matlab