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Association Rules. Presented by: Anilkumar Panicker. What is Data Mining??. Search for valuable information in large volumes of data. A step in knowledge discovery in databases.

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association rules

Association Rules

Presented by: Anilkumar Panicker

what is data mining
What is Data Mining??
  • Search for valuable information in large volumes of data.
  • A step in knowledge discovery in databases.
  • It enables companies to focus on customer satisfaction, corporate profits, and determining the impact of various parameters on the sales.
association rule
Association Rule
  • Association rules are used to show the relationships between data items.
  • Association rules detect common usage of data items.
  • E.g. The purchasing of one product when another product is purchased represents an association rule.
example 1
Example 1
  • Grocery store.
  • Association rules have most direct application in the retail businesses.
  • Association rules used to assist in marketing, advertising, floor placements and inventory control.
slide5
From the transaction history several association rules can be derived.
  • E.g. 100% of the time that PeanutButter is purchased, so is bread.
  • 33% of the time PeanutButter is purchased, Jelly is also purchased.
example 2
Example 2
  • A Telephone Company.
  • A telephone company must ensure that all calls are completed and in acceptable period of time.
  • In this environment, a potential data mining problem would be to predict a failure of a node.
  • This can be done by finding association rules of the type XFailure.
slide7
If these types of rules occur with a high confidence, Failures can be predicted.
  • Even though the support might be low because the X condition does not frequently occur.
association rule1
Association rule
  • Given a set of items I = {I1,I2,….Im} and a database of transactions D = {t1,t2,….tm} where ti = { Ii1,Ii2,….Iik} and IiJ € I , an association rule is an implication of the form X  Y where X,Y C I are sets of items called itemsets and X∩Y =ø.
slide9
Support (s):

The support (s) for an association rule

XY is the percentage of transactions in the database that contain X U Y.

E.g. If bread along with peanutbutter occurs in 60% of the total transactions, then the support for breadpeanutbutter is 60%

slide10
Confidence or Strength (α):

The confidence or strength (α) for an association rule XY is the ratio of the number of transactions that contain X U Y to the number of transactions that contain X.

Eg.if support for breadpeanutbutter is 60% and bread occurs in 80% of total transactions then confidence for breadpeanutbutter is 75%.

selecting association rules
Selecting Association rules
  • The selection of association rules is based on Support and Confidence.
  • Confidence measures the strength of the rule, Whereas support measures how often it should occur in the database.
  • Typically large confidence values and a smaller support are used.
  • Rules that satisfy both minimum support and minimum confidence are called strong rules.
association rule problem
Association Rule Problem
  • Given a set of Items I = {I1,I2,….Im} and a database of transactions D = {t1,t2,….tn} where ti = { Ii1,Ii2,….Iik} and IiJ € I . The association rule problem is to identify all association rules XY with a minimum support and confidence. These values (s,α) are given as input to the problem.
large itemsets
Large Itemsets
  • A Large Itemset / frequent Itemset is an itemset whose number of occurrences is above a threshold, s (Support)
  • Finding large Itemsets generally is quite easy but very costly.
  • The naive approach would be to count all itemsets that appear in any transaction.
  • Given a set of items of size m, there are 2m subsets. Ignoring the empty set we are still left with 2m – 1 subsets.
slide14
For e.g. In the retail store example if have set of items of size 5, i.e the store sells 5 products. Then the possible number of itemsets is 25 – 1 = 31.
  • If the 5 products sold are bread,peanutbutter,milk,beer and jelly.

then the 31 possible itemsets are

slide15
Bread
  • Peanutbutter
  • Milk
  • Beer
  • Jelly
  • Bread,peanutbutter
  • Bread,milk
  • Bread,beer
  • Bread,jelly
  • Peanutbutter,milk
  • Peanutbutter,beer
  • Peanutbutter,jelly
  • Milk,beer
  • Milk,jelly
  • Beer, jelly
  • Bread,peanutbutter,milk
  • Bread, Peanutbutter, beer and so on.
slide16
For m = 30 the number of potential itemsets become 1073741823.
  • The challenge in solving an association problem is hence to efficiently determining all large itemsets.
  • Most association rule algorithms are based on smart ways to reduce the number of itemsets to be counted.
large itemsets1
Large Itemsets
  • The most common approach to finding association rules is to breakup the problem into two parts
  • Finding large Itemsets and
  • Generating rules from these itemsets.
slide18
Subset of any large itemset is also large.
  • Once the large Itemsets have been found, we know that any interesting association rule, XY ,must have X U Y in this set of frequent itemsets.
  • When all large itemsets are found, generating the association rules is straightforward.
apriori algorithm
Apriori Algorithm
  • Apriori algorithm is the most well known association rule algorithm.
  • Apriori algorithm is used to efficiently discover large itemsets.
  • Apriori algorithm uses the property that any subset of a large itemset must be large.
  • Inputs: Itemsets, Database of transactions, support and the output is large itemsets.
references
References
  • Data Mining by Margaret Dunham.
  • Wikipedia
slide25
Q & A

…… Thanks..

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