FSKYMINE A Faster Algorithm For Mining Skyline Frequent Utility Itemsets

FSKYMINE A Faster Algorithm For Mining Skyline Frequent Utility Itemsets. PowerPoint Presentation FSKYMINE A Faster Algorithm For Mining Skyline Frequent Utility Itemsets Good morning, chair, ladies and gentlemen My name is Cheng Wei, Wu I am a PhD student from National Che.

FSKYMINE A Faster Algorithm For

Mining Skyline Frequent Utility

Itemsets

 Introduction

 Frequent itemsets mining (FIM)

 High-utility itemsets mining (HUIM)

 Skyline frequent utility itemsets mining (SFUIM)

1) Difficulty to specify the minSup value

 2) Ignoring the item utilities like weight, unit profit, and quantity, meanwhile such aspects are preferable in practical problems

High-utility itemsets mining (HUIM)

 Overcome the second limitation of FIM by using both profits and quantities of products in transactions to extract actual utility values of itemsets.

The problem of both FIM and HUIM it is that they require

choosing an threshold for minimum support and utility by users It is very difficul to choice an appropriate

threshold.

Skyline frequent utility itemsets mining (SFUIM)

Skyline frequent utility itemsets mining (SFUIM)

 Utility of an item ip in the transaction Td

 u(ip ,Td ) = q(ip, Td ) × p(ip)

 High Utility Itemset

 An itemset X is called a high utility itemset iff

u(X) > min_utiliy

i.e., min_utility = 30,

{B}: 16 is a low utility itemset ;

{BD}: 30 is a high utility itemset

Item A B C D E F G Unit

 Utility of an item ip in the transaction Td

 u(ip ,Td ) = q(ip, Td ) × p(ip)

 High Utility Itemset

 An itemset X is called a high utility itemset iff

u(X) > min_utiliy

i.e., min_utility = 30,

{B}: 16 is a low utility itemset ;

{BD}: 30 is a high utility itemset

Item A B C D E F G Unit

 Utility of an item ip in the transaction Td

 u(ip ,Td ) = q(ip, Td ) × p(ip)

 High Utility Itemset

 An itemset X is called a high utility itemset iff

u(X) > min_utiliy

i.e., min_utility = 30,

{B}: 16 is a low utility itemset ;

{BD}: 30 is a high utility itemset

Item A B C D E F G Unit

 Utility of an item ip in the transaction Td

 u(ip ,Td ) = q(ip, Td ) × p(ip)

 High Utility Itemset

 An itemset X is called a high utility itemset iff

u(X) > min_utiliy

i.e., min_utility = 30,

{B}: 16 is a low utility itemset ;

{BD}: 30 is a high utility itemset

Item A B C D E F G Unit

i.e., u({AD}) = u({AD}, T1) + u({AD}, T3 ) = 7 +

{BE}:31, {BCE}:37, {ACE}:31 {BD}:30, {BCD}:34, {BDE}:36 {BCDE}:40, {ABCDEF}:30

 An itemset X is said to dominate

another itemset Y in D, denoted as X≻Y

iff f(X)≥f(Y) and u(X) ≥u(Y).

 An itemset is skyline frequent utility

itemset iff it is not dominated by any

other itemset in the database

Item A B C D E F G Unit

 SKYMINE2 [9]

 Limitations: The algorithm performs numerous operations of joining two utility lists and generates numerous utility lists and potentials SFUIs.

10

[6] Vikram Goyal, Ashish Sureka, and Dhaval Patel Efficient skyline itemsets

mining In Proceedings of the Eighth International Conference

on Computer Science & Software Engineering, pages 119–124 ACM, 2015

[9] Jerry Chun-Wei Lin, Lu Yang, Philippe Fournier-Viger, Siddharth

Dawar, Vikram Goyal, Ashish Sureka, and Bay Vo A more efficient

algorithm to mine skyline frequent-utility patterns In International

Conference on Genetic and Evolutionary Computing, pages 127–135.

Springer, 2016.

Proposed Algorithm

FMSFUI (Faster Algorithm For Mining Skyline

Frequent-Utility Itemsets)

• We propose:

• a mechanism named remaining transaction-weighted

utility cooccurrence of pair item x, y in a database

SD is denoted as rtwuc(x, y ).

• And a data structure name extent utility list of an itemset in a DB

•

Proposed Algorithm

FMSFUI (Faster Algorithm For Mining Skyline Frequent-Utility Itemsets )

The remaining transaction-weighted utility

cooccurrence of pair item x; y in a database SD is

denoted as rtwuc(x, y) and defined as the sum of

the remaining transaction-weighted utility

co-occurrence of pair item x, y in all transactions

containing both of the item x; y in the database.

({AC},T2)=2*5+2*3+6*1=2 2

rtwuc ({AC},T3)=1*5+1*3+1*1=9

Caculate rtwuc(x,y)

Profi t

5 2 1 2 3 1 1

Trans(xy).rutils= Trans(y).rutils

Proposed Algorithm

FMSFUI (Faster Algorithm For Mining Skyline

Frequent-Utility Itemsets)

 The maximal utility of the frequency value r is

denoted as umax[r] and defined as the

maximal utility of itemsets having the same frequency value r.

sumItemUtils) Given an itemset Px having occurrence frequency is r If the sum of sumItemSetutils and sumItemUtils values of extent utility list of Px is higher than or equal to umax(r) then Px is a potential skyline frequent- utility itemset.

Proposed Algorithm

FMSFUI (Faster Algorithm For Mining Skyline

Frequent-Utility Itemsets)

itemsets Px and Py such that Px having occurrence frequency is r, Py having occurrence frequency is r1 If

min(Px.sumitemsetutils,

than umax(r) or umax(r1) then Pxy and all extensions of Pxy are not SFUIs.

Platform for Experiment

Intel® Core 5 Quad Processor @ 2.30 GHz

8 Gigabyte Memory Implement in Java LanguageRunning on Windows 10

Performance evaluation

 In this paper, we proposed a very fast algorithm namely

FSKYMINE for efficiently mining skyline frequent

utility itemsets.

 We proposed a mechanism named remaining

transaction-weighted utility cooccurrence of

pair item x, y in a database SD is denoted as rtwuc(x, y)

and a data structure name extent utility list of an itemset

in a DB And based on these, we develop strategy of

Pruning) to reduce the number of join operations in mining

process skyline frequent utility itemsets.

significantly outperforms SKYMINE2.

System Lab, NCKU, Taiwan

Thanks for your attention

Hung Manh Nguyen

Le Quy Don Technical University

Hanoi, Vietnam manhhungk12@mta.edu.vn

Anh Viet Phan

Le Quy Don Technical University

Hanoi, Vietnam anhpv@mta.edu.vn

Lai Van Pham

Military Science and Technology Institute

Hanoi, Vietnam garry@cinnamon.is