Machine Learning Tutorial

What is Machine Learning? Machine Learning Life Cycle Python Anaconda setup Difference between ML/ AI/ Deep Learning Understanding different types of Machine Learning Data Pre-processing Supervised Machine Learning

ML Regression Algorithm

Linear Regression

ML Classification Algorithm

Introduction to ML Classification Algorithm Logistic Regression Support Vector Machine Decision Tree Naïve Bayes Random Forest

ML Clustering Algorithm

Introduction to ML Clustering Algorithm K-means Clustering Hierarchical Clustering

ML Association Rule learning Algorithm

Introduction to association Rule Learning Algorithm

Miscellaneous

Top 5 programming languages and their libraries for Machine Learning Basics Vectors in Linear Algebra in ML Decision Tree Algorithm in Machine Learning Bias and Variances in Machine Learning Machine Learning Projects for the Final Year Students Top Machine Learning Jobs Machine Learning Engineer Salary in Different Organisation Best Python Libraries for Machine Learning Regularization in Machine Learning Some Innovative Project Ideas in Machine Learning What is Cross Compiler Decoding in Communication Process IPv4 vs IPv6 Supernetting in Network Layer TCP Ports TCP vs UDP TCP Working of ARP Hands-on Machine Learning with Scikit-Learn, TensorFlow, and Keras Kaggle Machine Learning Project Machine Learning Gesture Recognition Machine Learning IDE Pattern Recognition and Machine Learning a MATLAB Companion Chi-Square Test in Machine Learning Heart Disease Prediction Using Machine Learning Machine Learning and Neural Networks Machine Learning for Audio Classification Standardization in Machine Learning Student Performance Prediction Using Machine Learning Data Visualization in Machine Learning How to avoid over fitting In Machine Learning Machine Learning in Education Machine Learning in Robotics Network intrusion Detection System using Machine Learning

Machine Learning Clustering Algorithm

Introduction to ML Clustering Algorithm

Clustering falls under unsupervised learning methods. In this, the machine is provided with a set of unlabeled data, and the machine is required to extract the structure from the data from its own, without any external supervision.

It searches for similar patterns in the dataset and then forms a cluster of such samples by similar grouping attributes. In other words, Clustering is a process of grouping related data samples, such that data points in one group are of the same kind but are different from the data points in another group. An assumption is made, and based on that similarity of datapoints is constituted.

An example of a cluster system is given below, to get more clarity about the concept of Clustering;

An example of a cluster system

Different Methods of Cluster Formation

There are several methods for cluster formation that are given below;

1. Density-Based

In density-based methods, the clusters are built as a dense region having a relationship. They result in excellent accuracy and facilitate in merging two clusters. Its examples are DBSCAN (Density-Based Spatial Clustering of Applications with Noise), OPTICS (Ordering Points to Identify Clustering Structures), etc.

2. Hierarchical Based

In these methods, the clusters are constructed as a tree-like structure. There are two types of approaches, such as the Bottom-Up approach (Agglomerative) and the Top-Down approach (Divisive). For example, Clustering using Representatives (CURE), Balanced iterative Reducing Clustering using Hierarchies (BIRCH), etc. 

3. Partitioning

In these methods, the clusters are made by splitting the samples into K clusters, such that the no of splits will be equal to the no of clusters. Examples are: K-means Clustering and CLARANS (Clustering Large Applications based on randomized Search)

4. Grid

In these methods, clusters are molded as a grid. The clustering functions performed on grids are faster and are independent of datapoints. Examples are Statistical Information Grid (STING), Clustering in Quest (CLIQUE).

Application of Clustering

  1. For reducing and compressing data: The concept of Clustering is extensively used in the field image processing as well as in vector quantization for data reduction, compression, and summarization.
  2. Intermediary step for Data mining steps: Since cluster serves well in summarizing the data for classification, hypothesis generation, and testing, it proves to be an intermediate step for data mining tasks as well. 
  3. Biology: It works well in the classification of biological anatomies like plants and animals.
  4. Insurance: It is used to keep track of customers and their policies while recognizing the frauds.
  5. Study of Earthquake: It may help in grouping earthquake affected areas to ascertain dangerous zones.  
  6. Social Network Analysis: Based on the concept of Clustering, it helps in generating a series of images, videos, and audios.