C118 K means.txt

K-means clustering is a type of unsupervised learning, which is used when you have unlabeled data 
(i.e., data without defined categories or groups). The goal of this algorithm is to find groups in 
the data, with the number of groups represented by the variable K.

The K-means clustering algorithm is used to find groups which have not been explicitly labeled in the 
data. This can be used to confirm business assumptions about what types of groups exist or to identify 
unknown groups in complex data sets. 


STEP 1: Choose the random number of K (clusters)
STEP 2: Randomly select the center points for K clusters: 
https://drive.google.com/uc?export=vi
ew&id=1PQ28Olk1DQzXC0HnSEudZ
SUx4N8LKWfA


STEP 3: Assign each data point to the nearest centroid:
https://drive.google.com/uc?export=vi
ew&id=10AeUS7ARbR_EN9sNhtl7F8
cuOGDNhjOV


STEP 4: Shift the centroids a little for all the clusters.
https://drive.google.com/uc?export=vi
ew&id=1F7IfWlqj5JT8zqUVetHbZOvY
yDUvcBvU

STEP 5: Re-assign each data point to the new closest centroid. If any points
got reassigned, repeat `Step 4` again otherwise the model is ready.

https://drive.google.com/uc?export=vi
ew&id=1sJx_QRvVDFXE1Otm-uApU
0FvTMF4600t



SUMMARY : https://drive.google.com/uc?export=vi
ew&id=1ZOG4uYODnOBpJwfpjx5E3
TVN8qIx0EJw


Inertia measures how well a dataset was clustered by K-Means. 
It is calculated by measuring the distance between each data 
point and its centroid, squaring this distance, and summing these 
squares across one cluster.



Note: Python iloc() function enables us to select a particular cell of 
the dataset, that is, it helps us select a value that belongs to a 
particular row or column from a set of values of a data frame or dataset.





Applications:
kmeans algorithm is very popular and used in a variety of applications
such as market segmentation, document clustering, image segmentation 
and image compression, etc. The goal usually when we undergo a cluster
analysis is either:

Get a meaningful intuition of the structure of the data we’re dealing 
with.

Cluster-then-predict where different models will be built for different
subgroups if we believe there is a wide variation in the behaviors of 
different subgroups. An example of that is clustering patients into 
different subgroups and build a model for each subgroup to predict the 
probability of the risk of having heart attack.


Height    Weight
185        72
170        56
168        60
179        68
182        72
188        77
180        71
180        70
183        84
180        88
180        67
177        76