1 / 6

Using Attribute Value Lattice to Find Closed Frequent Itemsets – Lin, Hu, Louie

Using Attribute Value Lattice to Find Closed Frequent Itemsets – Lin, Hu, Louie. New Apporoach to Data Mining. Find closed frequent itemsets Search only the attribute-value lattice Enables finding only the non-redundant association rule set. Frequent and Closed Itemsets .

berit
Download Presentation

Using Attribute Value Lattice to Find Closed Frequent Itemsets – Lin, Hu, Louie

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Using Attribute Value Lattice to Find Closed Frequent Itemsets – Lin, Hu, Louie

  2. New Apporoach to Data Mining • Find closed frequent itemsets • Search only the attribute-value lattice • Enables finding only the non-redundant association rule set

  3. Frequent and Closed Itemsets • Frequent Itemset: Itemset that occurs in a user-specified percentage of the database • Closed Itemset: An itemset (A) that is identical to its closure Cl(A) • Closure of an Itemset Cl(A): all items that appear in all tuples that contain A. • Eg. Cl(A) = {1,3,4,5} • Cl(C) = { 1,2,3,4,5} • Cl(W) = {1,2,3,4,5} • Cl(A)Cl(C )  Cl(W) = {I,3,4,5} ACW is a closed frequent itemset.

  4. Partial order and lattice • Partial Order: A binary relation that is reflexive (a <=a ), antisymmetric (a<=b and b<=a, then a = b) and transitive (a<=b and b<=c, then a<=c) • Lattice: Partially ordered set in which non-empty finite subsets have a least upper bound and a greatest lower bound

  5. Lin’s Algorithm • Attribute value lattice constructed from database • Construct bitmap of each frequent itemset B(Ii) • Set level number Li of Ii to 1 • Nodes contain Ii , Li , and B(Ii) where B(Ii) > threshold • Sort the item in nodes based on bitcount • For each node, Ii , Li ,B(Ii) in nodes • For each sibling Ii • I = Ii  Ijand Bcomb = B(Ii) B(Ij) • If Bcomb> threshold • If B(Ii) = B(Ij) remove Ij from nodes replace Ii with I 2. If If B(Ii)  B(Ij) create an edge from Ii to Ij Lj = max(Lj , Lj + 1) 3. If B(Ij)  B(Ii) create an edge from Ij to Ii Li = max(L, Lj + 1)

  6. Lin’s Algorithm • Searches attribute value lattice to find closed frequent itemsets

More Related