Cluster Analysis in just 10 Minutes| Clustering tutorial for Beginners| Data Science with ExcelR
Clustering or Cluster Analysis can be considered the most important unsupervised learning problem; so, as every other problem of this kind, it deals with finding a structure in a collection of unlabeled data.
Clustering or cluster analysis can be divided into two subgroups :
1️⃣ Hard Clustering: In hard clustering, each data point either belongs to a cluster completely or not.
2️⃣ Soft Clustering: In soft clustering, instead of putting each data point into a separate cluster, a probability or likelihood of that data point to be in those clusters is assigned.
K-means Clustering is one of the simplest unsupervised learning algorithms that solve the most popular clustering problem. The procedure follows a simple and easy way to classify a given data set through a certain number of clusters (assume k clusters) fixed a priori. The main idea is to define k centroids, one for each cluster.
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🟢This tutorial video helps you learn the following topics:
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1️⃣ What is Cluster Analysis
2️⃣ Cluster Analysis Examples
3️⃣ Data Segmentation
4️⃣ Exploratory method
5️⃣ K-means clustering
6️⃣ Hierarchical Clustering
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🟢About the course
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Cluster Analysis or Clustering is an important and simplest method, again a small extraction under the huge domain of Data Science which is mainly used for segregating the like members from the unliked one using cluster analysis. Whereas Hierarchical Clustering or Hierarchical Cluster Analysis is defined as the advanced techniques of segregating the similar ones from the dissimilar ones and putting them in a cluster where the clusters are pre-ordered accordingly.
This tutorial is for both, Data Science for Beginners as well as for the advanced level learners who have had some experience in humongous Data Science. This video can be a quick recap for them as well. It’s altogether a combined Data Science Machine Learning Tutorials and can be used as a reference video in the future. In this video, you will come into many concepts and a quick walk over on What is Clustering along with its application and examples. How to segment data into various groups or clusters? Some interesting points on K-MeanClustering and brief case-studies on Exploratory Method and so on.
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