Association Rule Mining Max Miner Mining Association Rules
- Slides: 17
Association Rule Mining - Max. Miner
Mining Association Rules in Large Databases p Association rule mining p Algorithms Apriori and FP-Growth p Max and closed patterns p Mining various kinds of association/correlation rules
Max-patterns & Close-patterns If there are frequent patterns with many items, enumerating all of them is costly. p We may be interested in finding the ‘boundary’ frequent patterns. p Two types… p
Max-patterns Frequent pattern {a 1, …, a 100} (1001) + (1002) + … + (110000) = 2100 -1 = 1. 27*1030 frequent sub-patterns! p Max-pattern: frequent patterns without proper frequent super pattern p n n BCDE, ACD are max-patterns BCD is not a max-pattern Min_sup=2 Tid 10 20 30 Items A, B, C, D, E, A, C, D, F
Maximal Frequent Itemset An itemset is maximal frequent if none of its immediate supersets is frequent Maximal Itemsets Infrequent Itemsets Border
Closed Itemset p An itemset is closed if none of its immediate supersets has the same support as the itemset
Maximal vs Closed Itemsets Transaction Ids Not supported by any transactions
Itemsets Minimum support = 2 Closed but not maximal Closed and maximal # Closed = 9 # Maximal = 4
Maximal vs Closed Itemsets
Max. Miner: Mining Max-patterns p Idea: generate the complete setenumeration tree one level at a time, while prune if applicable. (ABCD) A (BCD) AB (CD) AC (D) AD () ABC (C) ABD () ACD () ABCD () BC (D) BD () BCD () C (D) CD ()
Local Pruning Techniques (e. g. at node A) Check the frequency of ABCD and AB, AC, AD. p If ABCD is frequent, prune the whole sub-tree. p If AC is NOT frequent, remove C from the parenthesis before expanding. (ABCD) A (BCD) AB (CD) AC (D) AD () ABC (C) ABD () ACD () ABCD () BC (D) BD () BCD () C (D) CD ()
Algorithm Max. Miner Initially, generate one node N= (ABCD) , where h(N)= and t(N)={A, B, C, D}. p Consider expanding N, p n n p If h(N) t(N) is frequent, do not expand N. If for some i t(N), h(N) {i} is NOT frequent, remove i from t(N) before expanding N. Apply global pruning techniques…
Global Pruning Technique (across sub-trees) p When a max pattern is identified (e. g. ABCD), prune all nodes (e. g. B, C and D) where h(N) t(N) is a sub-set of it (e. g. ABCD). (ABCD) A (BCD) AB (CD) AC (D) AD () ABC (C) ABD () ACD () ABCD () BC (D) BD () BCD () C (D) CD ()
Example (ABCDEF) A (BCDE) B (CDE) C (DE) Items Frequency ABCDEF 0 A 2 B 2 C 3 D 3 E 2 F 1 D (E) E () Tid Items 10 A, B, C, D, E 20 B, C, D, E, 30 A, C, D, F Min_sup=2 Max patterns:
Example (ABCDEF) A (BCDE) B (CDE) C (DE) AC (D) AD () D (E) E () Tid Items 10 A, B, C, D, E 20 B, C, D, E, 30 A, C, D, F Min_sup=2 Node A Items Frequen cy ABCDE 1 AB 1 AC 2 AD 2 AE 1 Max patterns:
Example (ABCDEF) A (BCDE) B (CDE) C (DE) AC (D) AD () D (E) E () Items Frequency BCDE 2 BD BE Items 10 A, B, C, D, E 20 B, C, D, E, 30 A, C, D, F Min_sup=2 Node B BC Tid Max patterns: BCDE
Example (ABCDEF) A (BCDE) B (CDE) C (DE) AC (D) AD () D (E) E () Tid Items 10 A, B, C, D, E 20 B, C, D, E, 30 A, C, D, F Min_sup=2 Node AC Items Frequen cy ACD 2 Max patterns: BCDE ACD
- Max miner algorithm
- Association rule mining tutorial
- Integrating classification and association rule mining
- Association rules definition
- Association rule mining definition
- Mining of association
- Fast algorithms for mining association rules
- Fast algorithms for mining association rules
- Association rules in data mining
- Association rules in data mining
- Association analysis advanced concepts
- Database vs data mining
- Fast algorithms for mining association rules
- Fast algorithms for mining association rules
- Local maximum and minimum vs. absolute maximum and minimum
- Kelvin rodolfo
- Kris miner
- Meni miner