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4.7 Apriori Algorithm

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  v Apriori Algorithm: - Ø The Apriori algorithm is an unsupervised machine learning algorithm used for association rule learning. Association rule learning is a data mining technique that identifies frequent patterns, connections and dependencies among different groups of items called itemsets in data. Ø It is also known a Downward closure property.   Ø The Association rule is a strategy for detecting patterns in huge data sets. it involves finding relationships between variables in the data and using those relationships to make predictions or decisions. The purpose of an association rule is to find rules that define the association between distinct elements in a data set.   Ø It is a classic association rule mining technique used to discover frequent itemsets (groups of items that often appear together) in transactional databases.   Ø It is widely used in market basket analysis, recommendation systems, and pattern reco...
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4.6 Minimum Spanning Tree

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  v Spanning Tree: - Ø A spanning tree is a subgraph of an undirected connected of Graph G, which has all the vertices along with minimum possible number of edges. Hence, a spanning tree does not have cycles and it also cannot be disconnected. Ø If any vertex is missed, it is not a spanning tree. Ø A disconnected graph does not have any spanning tree, as it cannot be spanned to all its vertices. Ø We have three spanning trees in one complete graph. Ø A complete undirected graph can have maximum  n n-2  number of spanning trees, where  n  is the number of nodes. Ø In the above example,  n is 3, hence  3 3-2 = 3  spanning trees are possible.   Minimum Spanning Tree (MST): - Ø A minimum spanning tree is a spanning tree, which the sum of the weights of the edge has minimum weight than all other spanning trees of the same graph. Ø The weight of the spanning tree is the sum of the weights given to the edges of the spanning tre...
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4.5 Hierarchical Clustering (HCA)

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Hierarchical Clustering (HCA): - Hierarchical clustering is an unsupervised machine learning algorithm used to group unlabeled data into clusters . (or) Hierarchical clustering is a technique that organizes data into a hierarchy of clusters , which is shown using a tree-like diagram called a dendrogram , without fixing the number of clusters in advance. It builds clusters in a step-by-step hierarchical structure The structure looks like a tree , called a dendrogram No need to predefine number of clusters (unlike K-Means) Dendrogram A tree-like diagram that shows how clusters are formed Y-axis → Distance between clusters X-axis → Data points  It helps to decide where to cut to get clusters. using hierarchical clustering: ·         Step 1: Start with individual clusters:  Each car type initially forms its own cluster.  ·         Step 2: Calculate distances:  Determine th...
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