Introduction to Machine Learning Approaches (Cont..) 2. Clustering Dr. Ankita jaiswal Introduction to Machine Learning Approaches (Cont..) Clustering Dr. Ankita jaiswal Introduction to Machine Learning Approaches (Cont..) -Clustering Cluster formation Method: • 1. Density-Based Methods: These methods consider the clusters as the dense region having some similarities and differences from the lower dense region of the space. These methods have good accuracy and the ability to merge two clusters. Example DBSCAN (Density-Based Spatial Clustering of Applications with Noise), OPTICS (Ordering Points to Identify Clustering Structure), etc. • 2. Hierarchical Based Methods: The clusters formed in this method form a tree-type structure based on the hierarchy. New clusters are formed using the previously formed one. It is divided into two category • Agglomerative (bottom-up approach) • Divisive (top-down approach) Examples: CURE (Clustering Using Representatives), BIRCH (Balanced Iterative Reducing Clustering and using Hierarchies), etc. Dr. Ankita jaiswal Introduction to Machine Learning Approaches (Cont..) -Clustering • 3. Partitioning Methods: These methods partition the objects into k clusters and each partition forms one cluster. This method is used to optimize an objective criterion similarity function such as when the distance is a major parameter. • Example: K-means, CLARANS (Clustering Large Applications based upon Randomized Search), etc. • 4. Grid-based Methods: In this method, the data space is formulated into a finite number of cells that form a grid-like structure. All the clustering operations done on these grids are fast and independent of the number of data objects. • Example- STING (Statistical Information Grid), wave cluster, CLIQUE (CLustering In Quest), etc. Dr. Ankita jaiswal Introduction to Machine Learning Approaches (Cont..) Clustering Density based cluster Grid Clustering Dr. Ankita jaiswal Introduction to Machine Learning Approaches (Cont..) -Clustering Dr. Ankita jaiswal o The silhouette plot displays a measure of how close each point in one cluster is to points in the neighboring clusters and thus provides a way to assess parameters like number of clusters visually. o The value of the silhouette coefficient is between [-1, 1]. A score of 1 denotes the best meaning that the data point is very compact within the cluster to which it belongs and far away from the other clusters. The worst value is -1. Values near 0 denote overlapping clusters Dr. Ankita jaiswal Introduction to Machine Learning Approaches (Cont..) -Clustering • Following are the most important clustering algorithms in ML 1. K Means Clustering: • K-Means Clustering is an Unsupervised Learning algorithm which groups the unlabeled dataset into different clusters. Here K defines the number of pre-defined clusters that need to be created in the process, as if K=2, there will be two clusters, and for K=3, there will be three clusters, and so on. • It is an iterative algorithm that divides the unlabeled dataset into k different clusters in such a way that each dataset belongs only one group that has similar properties. • It is a centroid-based algorithm, where each cluster is associated with a centroid. The main aim of this algorithm is to minimize the sum of distances between the data point and their corresponding clusters. Dr. Ankita jaiswal • 2. Mean Shift Clustering: • It is another powerful clustering algorithm used in unsupervised learning. Unlike K-means clustering, it does not make any assumptions hence it is a non-parametric algorithm. • Mean-shift algorithm basically assigns the datapoints to the clusters iteratively by shifting points towards the highest density of datapoints i.e. cluster centroid. • The difference between K-Means algorithm and Mean-Shift is that later one does not need to specify the number of clusters in advance because the number of clusters will be determined by the algorithm w.r.t data. • 3. Hierarchical Clustering • •It is another unsupervised learning algorithm that is used to group together the unlabeled data points having similar characteristics. Dr. Ankita jaiswal Introduction to Machine Learning Approaches (Cont..) -Clustering • Applications of Clustering in different fields : • Marketing: It can be used to characterize & discover customer segments for marketing purposes. • Biology: It can be used for classification among different species of plants and animals. • Libraries: It is used in clustering different books on the basis of topics and information. • Insurance: It is used to acknowledge the customers, their policies and identifying the frauds. • City Planning: It is used to make groups of houses and to study their values based on their geographical locations and other factors present • Earthquake studies: By learning the earthquake-affected areas we can determine the dangerous zones Dr. Ankita jaiswal Introduction to Machine Learning Approaches (Cont..) 3. Reinforcement Learning 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. In Reinforcement Learning, the agent learns automatically using feedbacks without any labeled data, unlike supervised learning. RL solves a specific type of problem where decision making is sequential, and the goal is long-term, such as game-playing, robotics, etc. 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. Thus, Reinforcement Learning(RL) is a type of machine learning technique that enables an agent to learn in an interactive environment by trial and error using feedback from its own actions and experiences It is a core part of Artificial intelligence, and all AI agent works on the concept of reinforcement learning. Dr. Ankita jaiswal Introduction to Machine Learning Approaches (Cont..) - Reinforcement Learning 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. • The agent continues doing these three things (take action, change state/remain in the same state, and get feedback), and by doing these actions, he learns and explores the environment. • The agent learns that what actions lead to positive feedback or rewards and what actions lead to negative feedback penalty. As a positive reward, the agent gets a positive point, and as a penalty, it gets a negative point. Dr. Ankita jaiswal Dr. Ankita jaiswal Dr. Ankita jaiswal Introduction to Machine Learning Approaches (Cont..) - Reinforcement Learning Dr. Ankita jaiswal Introduction to Machine Learning Approaches (Cont..) - Reinforcement Learning Dr. Ankita jaiswal • Key Features of Reinforcement Learning • In RL, the agent is not instructed about the environment and what actions need to be taken. • It is based on the hit and trial process. • The agent takes the next action and changes states according to the feedback of the previous action. • The agent may get a delayed reward. • The environment is stochastic (uncertain or random), and the agent needs to explore it to reach to get the maximum positive rewards. Dr. Ankita jaiswal Introduction to Machine Learning Approaches (Cont..) - Reinforcement Learning Dr. Ankita jaiswal • Terms used in Reinforcement Learning • Agent(): An entity that can perceive/explore the environment and act upon it. • Environment(): A situation in which an agent is present or surrounded by. In RL, we assume the stochastic environment, which means it is random in nature. • Action(): Actions are the moves taken by an agent within the environment. • State(): State is a situation returned by the environment after each action taken by the agent. • Reward(): A feedback returned to the agent from the environment to evaluate the action of the agent. • Policy(): Policy is a strategy applied by the agent for the next action based on the current state. • Value(): It is expected long-term retuned with the discount factor and opposite to the short-term reward. • Q-value(): It is mostly similar to the value, but it takes one additional parameter as a current action (a). Dr. Ankita jaiswal RL vs SL Dr. Ankita jaiswal Application of RL Dr. Ankita jaiswal Application of RL (Cont..) Robotics: RL is used in Robot navigation, Robo-soccer, walking, juggling, etc. Control: RL can be used for adaptive control such as Factory processes, admission control in telecommunication, and Helicopter pilot is an example of reinforcement learning. Game Playing: RL can be used in Game playing such as tic-tac-toe, chess, etc. Chemistry: RL can be used for optimizing the chemical reactions. Business: RL is now used for business strategy planning. Manufacturing: In various automobile manufacturing companies, the robots use deep reinforcement learning to pick goods and put them in some containers. Finance Sector: The RL is currently used in the finance sector for evaluating trading strategies. Dr. Ankita jaiswal Introduction to Machine Learning Approaches (Cont..) 4. Decision Tree Dr. Ankita jaiswal Introduction to Machine Learning Approaches (Cont..) -Decision Tree • Thus, it is a method for approximating discrete target function, in which the learned function is represented by a decision tree. • Set of IF-THEN rules are used to improve human readability. • It is an example of Inductive inference algorithm. Dr. Ankita jaiswal Introduction to Machine Learning Approaches (Cont..) -Decision Tree Dr. Ankita jaiswal Introduction to Machine Learning Approaches (Cont..) -Decision Tree Dr. Ankita jaiswal Introduction to Machine Learning Approaches (Cont..) -Decision Tree Dr. Ankita jaiswal Introduction to Machine Learning Approaches (Cont..) -Decision Tree Dr. Ankita jaiswal Introduction to Machine Learning Approaches (Cont..) -Decision Tree Dr. Ankita jaiswal Introduction to Machine Learning Approaches (Cont..) -Decision Tree Dr. Ankita jaiswal Introduction to Machine Learning Approaches (Cont..) -Decision Tree Decision for Accepting a Job Offer Dr. Ankita jaiswal
