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K Means Clustering Algorithm | K Means Example in Python | Machine Learning Algorithms | Edureka

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The document discusses the K-Means clustering algorithm. It begins by defining clustering as grouping similar data points together. It then describes K-Means clustering, which groups data into K number of clusters by minimizing distances between points and cluster centers. The K-Means algorithm works by randomly selecting K initial cluster centers, assigning each point to the closest center, and recalculating centers as points are assigned until clusters stabilize. The best number of K clusters is found through trial and error to minimize variation between points and clusters.

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Copyright © 2017, edureka and/or its affiliates. All rights reserved.
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Copyright © 2017, edureka and/or its affiliates. All rights reserved. ▪ What is Clustering? ▪ Types of Clustering ▪ What is K- Means Clustering? ▪ How does a K-Means Algorithm works? ▪ K-Means with Python Copyright © 2018, edureka and/or its affiliates. All rights reserved. Agenda of Today’s Session
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Copyright © 2018, edureka and/or its affiliates. All rights reserved. What is Clustering?
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“Clustering is the process of dividing the datasets into groups, consisting of similar data-points” What is Clustering? ▪ Points in the same group are as similar as possible ▪ Points in different group are as dissimilar as possible
Most read
5
What is Clustering? Group of diners in a restaurant Items arranged in a mall
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Where is it Used? Recommendation System Recommended Movies Fickr’s Photos
7
How business use Clustering? Insurance Companies Retail Store Banking
8
Types of Clustering Exclusive Clustering Overlapping Clustering Hierarchical Clustering Exclusive Clustering ▪ Hard Clustering ▪ Data Point / Item belongs exclusively to one cluster ▪ For Example: K-Means Clustering
Most read
9
Types of Clustering Exclusive Clustering Overlapping Clustering Hierarchical Clustering Overlapping Clustering ▪ Soft Cluster ▪ Data Point/ Item belongs to multiple cluster ▪ For Example: Fuzzy/ C-Means Clustering
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Types of Clustering Exclusive Clustering Overlapping Clustering Hierarchical Clustering Hierarchical Clustering 1 2 3 4
Most read
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What is K-Means Clustering? “K-Means is a clustering algorithm whose mail goal is to group similar elements or data points into a cluster.” NOTE: ‘K’ in K-Means represent the number of clusters
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What is K-Means Clustering? Pile of dirty clothes
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Where Can I apply K-Means? https://gifer.com/en/Ckp3 Document Classifier
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K-Means Algorithm Number of Clusters = 3
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K-Means Algorithm Number of Clusters, K = 3
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K-Means Algorithm Distance from point 1 to the red cluster Distance from point 1 to the blue cluster Distance from point 1 to the green cluster ▪ Step 1: Select the number of clusters to be identified, i.e select a value for K =3 in this case ▪ Step 2: Randomly select 3 distinct data point ▪ Step 3: Measure the distance between the 1st point and selected 3 clusters
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K-Means Algorithm Step 4: Assign the 1st point to nearest cluster (red in this case).
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K-Means Algorithm Step 5: Calculate the mean value including the new point for the red cluster
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K-Means Algorithm Distance from point 2 to the red cluster Distance from point 2 to the blue cluster Distance from point 2 to the green cluster Find to which cluster does point 2 belongs to, how? ▪ Repeat the same procedure but measure the distance to the red mean Add the point to the nearest cluster
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K-Means Algorithm Calculate the cluster mean including the new point
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K-Means Algorithm Find to which cluster does point 3 belongs to, how? ▪ Repeat the same procedure but measure the distance to the red mean
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K-Means Algorithm Measure the distance and add the 3rd point to the nearest cluster, (red)
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K-Means Algorithm Calculate the new cluster mean using the new point
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K-Means Algorithm To which cluster does this point belongs to? ▪ Measure the distance ▪ Assign the point to the nearest cluster ▪ Calculate the cluster mean using the new point
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K-Means Algorithm ▪ Measure the distance ▪ Assign the point to the nearest cluster ▪ Calculate the cluster mean using the new point
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K-Means Algorithm ▪ Measure the distance ▪ Assign the point to the nearest cluster ▪ Calculate the cluster mean using the new point
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K-Means Algorithm To which cluster does this point belongs to? ▪ Measure the distance from the cluster mean (centroids) ▪ Assign the point to the nearest cluster ▪ Calculate the cluster mean using the new point
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K-Means Algorithm ▪ Measure the distance from the cluster mean (centroids) ▪ Assign the point to the nearest cluster ▪ Calculate the cluster mean using the new point Since the point is located closet to green cluster
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K-Means Algorithm ▪ Measure the distance from the cluster mean (centroids) ▪ Assign the point to the nearest cluster ▪ Calculate the cluster mean using the new point
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K-Means Algorithm Since all of these points are located closet to green cluster so all of them will be assigned to green cluster
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K-Means Algorithm Original/Expected Result Result from 1st iteration
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K-Means Algorithm Total variation within the cluster According to the K-Means Algorithm it iterates over again and again unless and until the data points within each cluster stops changing
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K-Means Algorithm Iteration 2: Again we will start from the beginning. But this time we will be selecting different initial random point (as compared to what we chose in the 1st iteration) ▪ Step 1: Select the number of clusters to be identified, i.e. K =3 in this case ▪ Step 2: Randomly select 3 distinct data point ▪ Step 3: Measure the distance between the 1st point and selected 3 clusters
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K-Means Algorithm Algorithm picks 3 initial clusters and adds the remaining points to the cluster with the nearest mean, and again recalculating the mean each time a new point is added to the cluster
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K-Means Algorithm Algorithm picks 3 initial clusters and adds the remaining points to the cluster with the nearest mean, and again recalculating the mean each time a new point is added to the cluster
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K-Means Algorithm Algorithm picks 3 initial clusters and adds the remaining points to the cluster with the nearest mean, and again recalculating the mean each time a new point is added to the cluster
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K-Means Algorithm Algorithm picks 3 initial clusters and adds the remaining points to the cluster with the nearest mean, and again recalculating the mean each time a new point is added to the cluster Total variation within the cluster
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K-Means Algorithm Iteration 3: Again we will start from the beginning and select different initial random point (as compared to what we chose in the 1st and 2nd iteration) Pick 3 initial clusters
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K-Means Algorithm Cluster the remaining points
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K-Means Algorithm Total variation within the cluster Finally sum the variation within each cluster
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K-Means Algorithm The algorithm can now compare the result and select the best variance out of it 1st Iteration 2nd Iteration 3rd Iteration
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K-Means Algorithm Now what if we have our data plotted on the X and Y axis X-Axis Y-Axis
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K-Means Algorithm Similarly, pick initial 3 random points.. X-Axis Y-Axis
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K-Means Algorithm We will be using the Euclidean distance (in 2D its same as that of a Pythagorean Theorem) X-Axis Y-Axis ℎ2 = 𝑝2 + 𝑏2 p b
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K-Means Algorithm Again assign the point to the nearest cluster X-Axis Y-Axis
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K-Means Algorithm Finally calculate the centroid (mean of cluster) including the new point X-Axis Y-Axis
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K-Means Algorithm Finally in first iteration you get something like this…again you have to iterate this process to get the final cluster X-Axis Y-Axis
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How will you find K value In the previous scenario k value was known to be 3, but this is not always true
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How will you find K value For deciding the value of k, you have to use hit and trail method, starting from K = 1 K=1 is the worst case scenario, even you cross- verify it with total variation
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How will you find K value Now try with K = 2 K=2 is still better then K = 1 (Total Variation) K = 1 K = 2
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How will you find K value Now try with K = 3 K=3 is even better than K =2 (Total Variation) K = 1 K = 2 K = 3
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How will you find K value Now try with K = 4 Total variation in K=4 is less than K =3 K = 1 K = 2 K = 3 K = 4
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How will you find K value Now try with K = 4 Total variation in K=4 is less than K =3 K = 1 K = 2 K = 3 K = 4 Each time you increase the cluster the variation decreases, no. of clusters = no. of data points then in that case the variation = 0
54
How will you find K value Reductioninvariance Number of Cluster This point is the elbow point and it is used to determine the value of K(clusters)
55
Copyright © 2017, edureka and/or its affiliates. All rights reserved. DEMO Let’s learn to code © MadeByShape Ltd. Web Design Manchester 2018. All Rights Reserved.
56
K-Means Algorithm Summarizing the K-Means Algorithm
57
Copyright © 2017, edureka and/or its affiliates. All rights reserved.
Copyright © 2017, edureka and/or its affiliates. All rights reserved.
Copyright © 2017, edureka and/or its affiliates. All rights reserved. ▪ What is Clustering? ▪ Types of Clustering ▪ What is K- Means Clustering? ▪ How does a K-Means Algorithm works? ▪ K-Means with Python Copyright © 2018, edureka and/or its affiliates. All rights reserved. Agenda of Today’s Session
Copyright © 2018, edureka and/or its affiliates. All rights reserved. What is Clustering?
“Clustering is the process of dividing the datasets into groups, consisting of similar data-points” What is Clustering? ▪ Points in the same group are as similar as possible ▪ Points in different group are as dissimilar as possible
What is Clustering? Group of diners in a restaurant Items arranged in a mall
Where is it Used? Recommendation System Recommended Movies Fickr’s Photos
How business use Clustering? Insurance Companies Retail Store Banking
Types of Clustering Exclusive Clustering Overlapping Clustering Hierarchical Clustering Exclusive Clustering ▪ Hard Clustering ▪ Data Point / Item belongs exclusively to one cluster ▪ For Example: K-Means Clustering
Types of Clustering Exclusive Clustering Overlapping Clustering Hierarchical Clustering Overlapping Clustering ▪ Soft Cluster ▪ Data Point/ Item belongs to multiple cluster ▪ For Example: Fuzzy/ C-Means Clustering
Types of Clustering Exclusive Clustering Overlapping Clustering Hierarchical Clustering Hierarchical Clustering 1 2 3 4
What is K-Means Clustering? “K-Means is a clustering algorithm whose mail goal is to group similar elements or data points into a cluster.” NOTE: ‘K’ in K-Means represent the number of clusters
What is K-Means Clustering? Pile of dirty clothes
Where Can I apply K-Means? https://gifer.com/en/Ckp3 Document Classifier
K-Means Algorithm Number of Clusters = 3
K-Means Algorithm Number of Clusters, K = 3
K-Means Algorithm Distance from point 1 to the red cluster Distance from point 1 to the blue cluster Distance from point 1 to the green cluster ▪ Step 1: Select the number of clusters to be identified, i.e select a value for K =3 in this case ▪ Step 2: Randomly select 3 distinct data point ▪ Step 3: Measure the distance between the 1st point and selected 3 clusters
K-Means Algorithm Step 4: Assign the 1st point to nearest cluster (red in this case).
K-Means Algorithm Step 5: Calculate the mean value including the new point for the red cluster
K-Means Algorithm Distance from point 2 to the red cluster Distance from point 2 to the blue cluster Distance from point 2 to the green cluster Find to which cluster does point 2 belongs to, how? ▪ Repeat the same procedure but measure the distance to the red mean Add the point to the nearest cluster
K-Means Algorithm Calculate the cluster mean including the new point
K-Means Algorithm Find to which cluster does point 3 belongs to, how? ▪ Repeat the same procedure but measure the distance to the red mean
K-Means Algorithm Measure the distance and add the 3rd point to the nearest cluster, (red)
K-Means Algorithm Calculate the new cluster mean using the new point
K-Means Algorithm To which cluster does this point belongs to? ▪ Measure the distance ▪ Assign the point to the nearest cluster ▪ Calculate the cluster mean using the new point
K-Means Algorithm ▪ Measure the distance ▪ Assign the point to the nearest cluster ▪ Calculate the cluster mean using the new point
K-Means Algorithm ▪ Measure the distance ▪ Assign the point to the nearest cluster ▪ Calculate the cluster mean using the new point
K-Means Algorithm To which cluster does this point belongs to? ▪ Measure the distance from the cluster mean (centroids) ▪ Assign the point to the nearest cluster ▪ Calculate the cluster mean using the new point
K-Means Algorithm ▪ Measure the distance from the cluster mean (centroids) ▪ Assign the point to the nearest cluster ▪ Calculate the cluster mean using the new point Since the point is located closet to green cluster
K-Means Algorithm ▪ Measure the distance from the cluster mean (centroids) ▪ Assign the point to the nearest cluster ▪ Calculate the cluster mean using the new point
K-Means Algorithm Since all of these points are located closet to green cluster so all of them will be assigned to green cluster
K-Means Algorithm Original/Expected Result Result from 1st iteration
K-Means Algorithm Total variation within the cluster According to the K-Means Algorithm it iterates over again and again unless and until the data points within each cluster stops changing
K-Means Algorithm Iteration 2: Again we will start from the beginning. But this time we will be selecting different initial random point (as compared to what we chose in the 1st iteration) ▪ Step 1: Select the number of clusters to be identified, i.e. K =3 in this case ▪ Step 2: Randomly select 3 distinct data point ▪ Step 3: Measure the distance between the 1st point and selected 3 clusters
K-Means Algorithm Algorithm picks 3 initial clusters and adds the remaining points to the cluster with the nearest mean, and again recalculating the mean each time a new point is added to the cluster
K-Means Algorithm Algorithm picks 3 initial clusters and adds the remaining points to the cluster with the nearest mean, and again recalculating the mean each time a new point is added to the cluster
K-Means Algorithm Algorithm picks 3 initial clusters and adds the remaining points to the cluster with the nearest mean, and again recalculating the mean each time a new point is added to the cluster
K-Means Algorithm Algorithm picks 3 initial clusters and adds the remaining points to the cluster with the nearest mean, and again recalculating the mean each time a new point is added to the cluster Total variation within the cluster
K-Means Algorithm Iteration 3: Again we will start from the beginning and select different initial random point (as compared to what we chose in the 1st and 2nd iteration) Pick 3 initial clusters
K-Means Algorithm Cluster the remaining points
K-Means Algorithm Total variation within the cluster Finally sum the variation within each cluster
K-Means Algorithm The algorithm can now compare the result and select the best variance out of it 1st Iteration 2nd Iteration 3rd Iteration
K-Means Algorithm Now what if we have our data plotted on the X and Y axis X-Axis Y-Axis
K-Means Algorithm Similarly, pick initial 3 random points.. X-Axis Y-Axis
K-Means Algorithm We will be using the Euclidean distance (in 2D its same as that of a Pythagorean Theorem) X-Axis Y-Axis ℎ2 = 𝑝2 + 𝑏2 p b
K-Means Algorithm Again assign the point to the nearest cluster X-Axis Y-Axis
K-Means Algorithm Finally calculate the centroid (mean of cluster) including the new point X-Axis Y-Axis
K-Means Algorithm Finally in first iteration you get something like this…again you have to iterate this process to get the final cluster X-Axis Y-Axis
How will you find K value In the previous scenario k value was known to be 3, but this is not always true
How will you find K value For deciding the value of k, you have to use hit and trail method, starting from K = 1 K=1 is the worst case scenario, even you cross- verify it with total variation
How will you find K value Now try with K = 2 K=2 is still better then K = 1 (Total Variation) K = 1 K = 2
How will you find K value Now try with K = 3 K=3 is even better than K =2 (Total Variation) K = 1 K = 2 K = 3
How will you find K value Now try with K = 4 Total variation in K=4 is less than K =3 K = 1 K = 2 K = 3 K = 4
How will you find K value Now try with K = 4 Total variation in K=4 is less than K =3 K = 1 K = 2 K = 3 K = 4 Each time you increase the cluster the variation decreases, no. of clusters = no. of data points then in that case the variation = 0
How will you find K value Reductioninvariance Number of Cluster This point is the elbow point and it is used to determine the value of K(clusters)
Copyright © 2017, edureka and/or its affiliates. All rights reserved. DEMO Let’s learn to code © MadeByShape Ltd. Web Design Manchester 2018. All Rights Reserved.
K-Means Algorithm Summarizing the K-Means Algorithm
Copyright © 2017, edureka and/or its affiliates. All rights reserved.
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