Hierarchical clustering algorithm.pptx
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Hierarchical clustering algorithms arrange data elements into a hierarchical tree structure called a dendrogram. Agglomerative algorithms start by placing each element in its own cluster and then iteratively merging the closest pairs of clusters based on the distance between elements. This results in a dendrogram showing all possible clusterings at varying distance thresholds. The single link technique for determining cluster distances merges clusters based on the minimum distance between any two points in different clusters.
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}
}
}
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Lecture1.pptMinakshee Patil This document discusses classification algorithms. It introduces statistical, distance-based, tree-based, rule-based, neural network-based, and combining classification techniques. It defines classification as mapping input data to classes. The problem is solved in two phases: creating a model from training data and applying the model to classify new data. Issues like missing data and performance measurement are also covered. Performance is measured by calculating classification accuracy as a percentage of correctly classified instances using a confusion matrix of true positives, false positives, true negatives and false negatives.
Unit 1.ppt
Unit 1.pptMinakshee Patil Data structures allow for the efficient organization and storage of data in a computer's memory. They include primitive structures like integers and strings, as well as non-primitive structures like arrays, linked lists, stacks, queues, trees, and graphs. Operations on data structures include traversing, searching, inserting, deleting, sorting, and merging. Abstract data types define the data type and operations that can be performed without specifying how they are implemented, separating the interface from the underlying implementation.
Lecture2 (1).ppt
Lecture2 (1).pptMinakshee Patil Data mining is the process of extracting patterns from large data sets to identify useful information. It involves applying machine learning algorithms to detect patterns in sample data and then using the learned patterns to predict future behaviors or outcomes. Data mining utilizes techniques from machine learning, statistics, databases, and visualization to analyze large datasets and discover hidden patterns. The goal of data mining is to extract useful information from large datasets and transform it into an understandable structure for further use.
Lecture3 (3).ppt
Lecture3 (3).pptMinakshee Patil This document discusses different classification algorithms including distance-based classification, k-nearest neighbors (KNN) classification, and decision tree classification. Distance-based classification assigns items to the closest class centroid. KNN classification assigns a new item to the class containing the majority of its k nearest neighbors. Decision tree classification uses a tree structure to partition the space and assign items to a class based on the region they fall in. The document provides details on how KNN and decision trees are constructed and issues to consider for each approach.
Lecture4.ppt
Lecture4.pptMinakshee Patil The document discusses classification using decision trees. It describes how decision trees are constructed to model the classification process, with internal nodes labeled by attributes and leaf nodes labeled by class. Popular decision tree algorithms like ID3, C4.5, and CART are described, along with their advantages of being easy to interpret and scale well, and disadvantages like difficulty handling continuous data. The basic steps of decision tree induction and application are outlined.
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Hierarchical clustering algorithm.pptx
- 2. Similarity and Distance Measures
- 3. Hierarchical Algorithms • A tree data structure, called a dendrogram, can be used to illustrate the hierarchical clustering technique • The root in a dendrogram tree contains one cluster where all elements are together. • The leaves in the dendrogram each consist of a single element cluster. • Internal nodes in the dendrogram represent new clusters formed by merging the clusters that appear as its children in the tree. • Each level in the tree is associated with the distance measure that was used to merge the clusters. • All clusters created at a particular level were combined because the children clusters had a distance between them less than the distance value associated with this level in the tree
- 4. Example
- 5. Agglomerative Algorithms • Agglomerative algorithms start with each individual item in its own cluster and iteratively merge clusters until all items belong in one cluster. • It assumes that a set of elements and distances between them is given as input adjacency matrix. • The output of the algorithm is a dendrogram, which produces a set of clusters rather than just one clustering. • The user can determine which of the clusters (based on distance threshold) he or she wishes to use. • In addition, the technique used to determine the distance between clusters may vary: Single link, complete link, and average link techniques
- 7. Single Link Technique • The single link technique is based on the idea of finding connected components in a graph. • A connected component is a graph in which there exists a path between any two vertices. • With this approach, two clusters are merged if the minimum distance between any two points is less than or equal to the threshold distance being considered. • For this reason, it is often called the nearest neighbor clustering technique
- 8. Example