CanTree: a tree structure for efficient incremental mining of frequent patterns

Carson Kai-Sang Leung, Quamrul I. Khan, Tariqul Hoque

ICDM’05

報告者：林靜怡2006/11/15

Introduction

Many existing incremental mining algorithms are Apriori-based

not easily adoptable to FP-tree based frequent-pattern mining

Related Work

The FELINE Algorithm with the CATS Tree

The AFPIM Algorithm

The FELINE Algorithm with the CATS Tree

CATS tree (Compressed and Arranged Transaction Sequences tree) Allows frequent-pattern mining without t

he generation of candidate itemsets requires one database scan to build the t

ree

CATS Tree New transactions are added at the root level At each level, items of the new transaction are

compared with children (or descendant) nodes. If the same items exist in both

1.the transaction is merged with the node at the

highest frequency level 2.The remainder of the transaction is then

added to the merged nodes repeated recursively until all common items

are found.

CATS Tree Any remaining items of the transaction

are added as a new branch to the last merged node.

The frequency of a node is lower than or equal to the frequencies of its ancestors

If the frequency of a node becomes higher than its ancestors, then it has to swap with the ancestors

Weaknesses

tree construction could be computationally

expensive checks existing tree paths one-by-one u

ntil a mergeable one is found extra cost is required for the swapping o

r merging of nodes.

The AFPIM Algorithm Adjusting FP-tree for Incremental Mining all the “frequent” items are arranged in

descending order of their global frequency when the ordering is changed, items in the

tree need to be adjusted When previously infrequent item becomes

“frequent” in the updated database, it needs to rescan and build a new FP-tree.

preMinsup:35%minsup:55%

4 x 0.35 = 1.4

Weaknesses

the amount of computation spent on swapping, merging, and splitting tree nodes

requirement for an additional mining parameter preMinsup finding an appropriate value for this para

meter is not easy

Weaknesses

when the database is updated, item frequencies may have changed. This results in changes in the ordering.

Both FELINE and AFPIM algorithms need lots of swapping, merging, and splitting of tree nodes

Canonical-Order Tree (CanTree) requires one database scan items are arranged according to some

canonical order in lexicographic order or alphabeticalord

er some specific order depending on the ite

m properties

Property Property 1 The ordering of items is unaffected by

the changes in frequency caused by incremental updates.

Property 2 The frequency of a node in the CanTre

e is at least as high as the sum of frequencies of its children.

CanTree Transactions can be easily added to t

he CanTree without any extensive searches for mergeable paths

mine frequent patterns from the tree in a fashion similar to FP-growth(a divide-and-conquer approach).

g: eg,deg,cdeg,bcdeg,abcdege: de,cde,bcde,abcde,ce, bce,abce,de,bde,abdef: ef,def,bdef,abdefd: cd,bcd,abcd,bd,abdc: bc,abcb: ab

Discussion CanTrees can be used for incremental constrained mining Efficiency and Memory Issues

On the surface, it appears CanTree may take a large amount of memory.

CanTree may not be as compact as the CATS tree,but it significantly reduce computation and time

assume we have enough main memory space

Experiment Database:generated by the program develo

ped at IBM Almaden Research Center consists of 1M records with an average transaction length of 10 items and a domain

of 1000 items time-sharing environment in a 1 GHz machi

ne

Experiment

Experiment

Experiment

Experiment

Conclusion

provide the user with a simple, but powerful, tree structure for efficient FP-tree based incremental mining

CanTree can be easily maintained Can used for efficient incremental con

strained mining