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     • This data mining technique helps to discover a link between two or more items. It finds a hidden pattern in the data set. Association rules are if-then statements that support to show the probability of interactions between data items within large data sets in different types of databases.
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2. Top results related to association rule mining javatpoint

   • Association Rule Mining in R Programming

     Association Rule Mining in R Language is an Unsupervised Non-linear algorithm to uncover how the items are associated with each other. In it, frequent Mining shows which items appear together in a transaction or relation. It’s majorly used by retailers, grocery stores, an online marketplace that has a large transactional database. The same way when any online social media, marketplace, and e-commerce websites know what you buy next using recommendations engines. The recommendations you get on item or variable, while you check out the order is because of Association rule mining boarded on past customer data. There are three common ways to measure association:

     • Support
     • Confidence
     • Lift

     Theory

     In association rule mining, Support, Confidence, and Lift measure association.

     Support says how popular an item is, as measured in the proportion of transactions in which an item set appears.

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     Confidence says how likely item Y is purchased when item X is purchased, expressed as {X -> Y}. Thus it is measured by the proportion of transaction with item X in which item Y also appears. Confidence might misrepresent the importance of association.

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     Lift says how...

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   • Association Rule Mining (Confidence and Lift)

     Top Answer

     Answered Oct 02, 2017 · 1 votes

     it depends on what the aim is of the association rule mining is:

     e.g.:

     - 100.000 transactions' database- - 2.000 tranasctions contain {(a, b)}- - 800 transactions contain {(a, b, c)}

     support of itemset {(a, b, c)}: (800 / 100.000) * 100 = 0,8%.

     the support of an itemset indicates how often a random transaction of the database contains the items of the itemset.

     confidence of association rule {(a, b)} -> {(c)}: (800 / 2000) * 100 = 40%.

     the confidence of an association rule indicates how often a random transaction of the dabase that contains the consequent of an association rule also contains the ancedent of the association rules.

     lift of association rule {(a, b)} -> {(c)}: 40 / ((5.000 / 100.000) * 100) = 8.

     the lift is the ratio of the confidence to the expected confidence of an association rule. the confidence of the association rule is 40%. expected confidence in this context means that if {(a, b)} occurs in a transaction that this does not increases the pobability of that {(c)} occurs in this transaction as well.

     e.g. if {(c)} occurs in 5.000 transactions of the database then the expected confidence is (100.000 / 5.000) * 100 = 5%.

     a lift value of an asscoiation rule which is higher then 1 indicates that the association rule is useful. a lift value less or equal 1 indicates that the association rule is not useful. in this case it is like the antecedent and the consequent of the association rule are independent of each other. the usefulness of the indication of the association rule that if a transaction holds ({a, b}) that it then associates ({c}) is not more useful then that ({a, b}) accociates ({c}) by chance.

     e.g. if all 100.000 transactions of the database contain {(c)} the expected value of {(c)} is (100.000 / 100.000) * 100 = 100%. the lift is 40 / 100 = 0,4. this is less then 1. therefore the association rule {(a, b)} -> {(c)} is not useful. {(c)} is in every transaction. if there is {(a, b)} in a transaction there is {(c)} in it either way. no use of an association.

     here the circle closes: it depends on the aim of the association rule mining. if the aim is to create extra strong association rules the confidence needs to be extra high. if the purpose is to create extra useful asscociation rules the lift needs to be extra high.

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     2/5

   • Association Rule Mining on Categorical Data with a High Number of Values for Each Attribute

     Top Answer

     Answered Jan 13, 2015 · 2 votes

     Association rule mining doesn't use "attributes". It processes market basket type of data. It does not make sense to preprocess it to binary attributes. Because you would need to convert the binary attributes into items again (worst case, you would then tranlate your "color=blue" item into "color_red=0, color_black=0, ... color_blue=1" if you are also looking for negative rules.

     Different algorithms - and different implementations of the theoretically same algorithm, unfortunately - will scale very differently.

     APRIORI is designed to scale well with the number of transactions, but not very well with the number of different items that have minimum support; in particular if you are expecting short itemsets to be frequent only. Other algorithms such as Eclat and FP-Growth may be much better there. But YMMV.

     First, try to convert the data set into a market basket format, in a way that you consider every item to be relevant. Discard everything else. Then start with a high minimum support, until you start getting results. Running with a too low minimum support may just run out of memory, or may take a long time.

     Also, make sure to get a good implementation. A lot of things that claim to be APRIORI are only half of it, and are incredibly slow.

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     3/5

   • Association Rule Mining in python on Census Data

     Top Answer

     Answered Jul 19, 2020 · 1 votes

     I tried many ways and a friend of mine suggested me a way for solving my problem. It worked in a graceful way, but yes it requires your logic too. This is the link which helped me to implement my question in a better way, here it is. In my solution I used correlating parameters and created a basket.

     This is it. Thank you.

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     4/5

   • Sentiment analysis with association rule mining

     Top Answer

     Answered May 25, 2014 · 1 votes

     Finding the top-3 most used adjectives for each product is not association rule mining.

     For Apriori to yield good results, you must be interested in itemsets of length 4 and more. Apriori pruning starts at length 3, and begins to yield major gains at length 4. At length 2, it is mostly enumerating all pairs. And if you are only interested in pairs (product, adjective), then apriori is doing much more work than necessary.

     Instead, use counting. Use hash tables. If you really have Exabytes of data, use approximate counting and heavy hitter algorithms. (But most likely, you don't have exabytes of data after extracting those pairs...)

     Don't bother to investigate association rule mining if you only need to solve this much simpler problem.

     Association rule mining is really only for finding patterns such as

     pasta, tomato, onion -> basil

     and more complex rules. The contribution of Apriori is to reduce the number of candidates when going from length n-1 -> n for length n > 2. And it gets more effective when n > 3.

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     5/5

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3. People also ask

   How do association rule mining algorithms work?

   • Association rule mining algorithms, such as Apriori or FP-Growth, are used to find frequent item sets and generate association rules. These algorithms work by iteratively generating candidate item sets and pruning those that do not meet the minimum support threshold.

   Frequent Item set in Data set (Association Rule Mining)

   www.geeksforgeeks.org/frequent-item-set-in-data-set-association-rule-mining/

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   What is association rule mining?

   • Association rule mining finds interesting associations and relationships among large sets of data items. This rule shows how frequently a itemset occurs in a transaction. A typical example is a Market Based Analysis.

   Association Rule - GeeksforGeeks

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   What is multi-level association rule mining?

   • This is used to find relationships between items at different levels of granularity. For example, in a retail dataset, multi-level Association Rule mining can be used to find relationships between individual items and categories of items.

   Multilevel Association Rule in data mining - Online Tutorials Library

   www.tutorialspoint.com/multilevel-association-rule-in-data-mining

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   What is multi-dimensional association rule mining?

   • This is used to find relationships between items in different dimensions of a dataset. For example, in a sales dataset, multi-dimensional Association Rule mining can be used to find relationships between products, regions, and time. This is used to find relationships between items at different levels of granularity.

   Multilevel Association Rule in data mining - Online Tutorials Library

   www.tutorialspoint.com/multilevel-association-rule-in-data-mining

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4. www.javatpoint.com › association-rule-learningAssociation Rule Learning - Javatpoint

   www.javatpoint.com › association-rule-learning

   • Cached

   Association rule learning is a type of unsupervised learning technique that checks for the dependency of one data item on another data item and maps accordingly so that it can be more profitable. It tries to find some interesting relations or associations among the variables of dataset.

   • Apriori Algorithm

     The primary objective of the apriori algorithm is to create...

   • Apriori Algorithm in Machine Learning

     The Apriori algorithm uses frequent itemsets to generate...

5. www.geeksforgeeks.org › association-ruleAssociation Rule - GeeksforGeeks

   www.geeksforgeeks.org › association-rule

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   Jan 11, 2023 · Association rule mining finds interesting associations and relationships among large sets of data items. This rule shows how frequently a itemset occurs in a transaction. A typical example is a Market Based Analysis.

6. Videos

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     18:40

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     Introduction to Association Rule Mining and Apriori Algorithm by Mahesh Huddar

     Jun 4, 2020

     15.2K Views

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     Apriori Algorithm Explained | Association Rule Mining | Finding Frequent Itemset | Edureka

     Jun 19, 2019

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     17:08

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     1. Association Rule Mining – Apriori Algorithm - Numerical Example Solved by Mahesh Huddar

     Jun 4, 2020

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7. www.geeksforgeeks.org › types-of-association-rulesTypes of Association Rules in Data Mining - GeeksforGeeks

   www.geeksforgeeks.org › types-of-association-rules

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   Jun 22, 2022 · Types of Association Rules in Data Mining. Association rule learning is a machine learning technique used for discovering interesting relationships between variables in large databases. It is designed to detect strong rules in the database based on some interesting metrics.

8. www.geeksforgeeks.org › frequent-item-set-in-dataFrequent Item set in Data set (Association Rule Mining)

   www.geeksforgeeks.org › frequent-item-set-in-data

   • Cached

   Feb 3, 2023 · Association rule mining algorithms, such as Apriori or FP-Growth, are used to find frequent item sets and generate association rules. These algorithms work by iteratively generating candidate item sets and pruning those that do not meet the minimum support threshold.

9. www.tutorialspoint.com › multilevel-associationMultilevel Association Rule in data mining

   www.tutorialspoint.com › multilevel-association

   • Cached

   May 18, 2023 · Association rule mining is used to discover relationships between items in a dataset. An association rule is a statement of the form "If A, then B," where A and B are sets of items. The strength of an association rule is measured using two measures: support and confidence.