Application of Clustering in Machine Learning

Application of Clustering

Below are some commonly known applications of clustering technique in Machine Learning:

In Identification of Cancer Cells: The clustering algorithms are widely used for the identification of cancerous cells. It divides the cancerous and non-cancerous data sets into different groups.

In Search Engines: Search engines also work on the clustering technique. The search result appears based on the closest object to the search query. It does it by grouping similar data objects in one group that is far from the other dissimilar objects. The accurate result of a query depends on the quality of the clustering algorithm used.

Customer Segmentation: It is used in market research to segment the customers based on their choice and preferences.

In Biology: It is used in the biology stream to classify different species of plants and animals using the image recognition technique.

In Land Use: The clustering technique is used in identifying the area of similar lands use in the GIS database. This can be very useful to find that for what purpose the particular land should be used, that means for which purpose it is more suitable

o Reinforcement Learning is a feedback-based Machine learning technique in which an agent learns to behave in an environment by performing the actions and seeing the results of actions. For each good action, the agent gets positive feedback, and for each bad action, the agent gets negative feedback or penalty.
o In Reinforcement Learning, the agent learns automatically using feedbacks without any labeled data, unlike supervised learning.
o Since there is no labeled data, so the agent is bound to learn by its experience only.
o RL solves a specific type of problem where decision making is sequential, and the goal is long-term, such as game-playing, robotics, etc.
o The agent interacts with the environment and explores it by itself. The primary goal of an agent in reinforcement learning is to improve the performance by getting the maximum positive rewards.
o The agent learns with the process of hit and trial, and based on the experience, it learns to perform the task in a better way. Hence, we can say that “Reinforcement learning is a type of machine learning method where an intelligent agent (computer program) interacts with the environment and learns to act within that.” How a Robotic dog learns the movement of his arms is an example of Reinforcement learning.
o It is a core part of Artificial intelligence, and all AI agent works on the concept of reinforcement learning. Here we do not need to pre-program the agent, as it learns from its own experience without any human intervention.

o Example: Suppose there is an AI agent present within a maze environment, and his goal is to find the diamond. The agent interacts with the environment by performing some actions, and based on those actions, the state of the agent gets changed, and it also receives a reward or penalty as feedback.

Clustering Algorithms

The Clustering algorithms can be divided based on their models that are explained above. There are
different types of clustering algorithms published, but only a few are commonly used. The clustering
algorithm is based on the kind of data that we are using. Such as, some algorithms need to guess the
number of clusters in the given dataset, whereas some are required to find the minimum distance
between the observation of the dataset.

Here we are discussing mainly popular Clustering algorithms that are widely used in machine learning:

#1. K-Means algorithm:

The k-means algorithm is one of the most popular clustering algorithms. It classifies the dataset by dividing the samples into different clusters of equal variances. The number of clusters must be specified in this algorithm. It is fast with fewer computations required, with the linear complexity of O(n).

#2. Mean-shift algorithm:

Mean-shift algorithm tries to find the dense areas in the smooth density of data 144 points. It is an example of a centroid-based model, that works on updating the candidates for centroid to be the center of the points within a given region.

#3. DBSCAN Algorithm:

It stands for Density-Based Spatial Clustering of Applications with Noise. It is an example of a density-based model similar to the mean-shift, but with some remarkable advantages. In this algorithm, the areas of high density are separated by the areas of low density. Because of this, the clusters can be found in any arbitrary shape.

#4. Expectation-Maximization Clustering using GMM:

This algorithm can be used as an alternative for the k-means algorithm or for those cases where K-means can be failed. In GMM, it is assumed that the data points are Gaussian distributed.

#5. Agglomerative Hierarchical algorithm:

The Agglomerative hierarchical algorithm performs the bottomup hierarchical clustering. In this, each data point is treated as a single cluster at the outset and then successively merged. The cluster hierarchy can be represented as a tree-structure

#6. Affinity Propagation:

It is different from other clustering algorithms as it does not require to specify the number of clusters. In this, each data point sends a message between the pair of data points until convergence. It has O(N2 T) time complexity, which is the main drawback of this algorithm.