OpenCV Machine Learning Note(III) K-Means

Q1: What is Clustering?

find k labels of a set of data and divide data to k by labels
like T-shirt size example in document

Q2: Why do we need clustering?

it gives a label to the raw data

considering in knn, svm training data stage, we need some responses to tell us what is data, how can we get responses?

1. assign data one by one manually
2. some algorithm categorizes data set automatically

clustering is the second choice

Q3: Why does k-means sound so familiar?

in video chapter, we use mean-shift track motive object track.actually, mean-shift and k-means clustering have some similar points

1. they all calculate the mean value and repeat
2. they all need criteria to stop repeat
3. they all shit the center in every repeat

Q4: What’s the main problem of k-means clustering?

they are two problems of k-means clustering

1. how to choose the category number k
2. how to choose the initial centroids of each category

Q5: Do we have other clustering algorithms?

yes, something like

• Mean-Shift Clustering
• Density-Based Spatial Clustering of Applications with Noise (DBSCAN)
• Expectation–Maximization (EM) Clustering using Gaussian Mixture Models (GMM)
• Agglomerative Hierarchical Clustering

赏

请问老板还招人么(/ω＼)

支付宝

微信