Association rule learning is an unsupervised learning technique that examines the dependency of one data item on another and maps accordingly to make it more profitable. It tries to discover some interesting relationships or links between the dataset's variables.
Association Rules Come in a Variety of Forms:
Rules for multi-relational association.
Association norms that are universal.
Quantitative connection is the only way to go.
Information association rules based on intervals.
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The challenge of uncovering intriguing correlations in vast datasets is known as association analysis. There are two types of interesting relationships: frequent item sets and association rules. A frequent item set is a group of objects that appear frequently together.
An itemset is a collection of zero or more items in association analysis. A k-itemset is an itemset that has k items in it. A 3-itemset, for example, might be Beer, Diapers, and Milk. A null (or empty) set is an itemset that has no items in it.
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Hi Kushal ! Your way of teaching is extremely helpful and you are one of the best teacher in the world.
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Very helpful and easy to understand all the concepts, best teacher for learning ML.
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explained everything in detail. I have a question learnvern provide dataset , and ppt ? or not?
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very nicely explained
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One importing thing to notify to team is by mistakenly navie's practical has been added under svm lecture and vice versa (Learning Practical 1)
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Shaga Chandrakanth Goud
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Hi Kushal ji, Thanks a lot for a very good explanation. I have doubts about where we can get the dataset that you explained in the video. Can you make it available in resource ,so that we can downld
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