1. Efficient Parallel Algorithm for Mining High Utility Patterns Based on Spark
Junqiang Liu, Rong Zhao, Xiangcai Yang, Yong Zhang, Xiaoning Jiang
Zhejiang Gongshang University, Hangzhou , China
浙江工商大学信电学院
23 June 2019

2. Content Motivation Problem Statement & Preliminaries
High Utility Pattern Mining, Sequential Algorithms, Frameworks
Our Mining Approach
New Parallel Algorithm Based on Spark
Experimental Evaluation
Conclusion and Future Work
References

3. Motivation High Utility Pattern Mining vs Frequent Pattern Mining
Utility = user’s interest + statistical significance HUP
Support = statistical significance only FP
HUP Mining much harder than FP Mining
Anti-monotonicity is satisfied for FP support of a pattern  support of its sub-pattern
Anti-monotonicity is not satisfied with HUP utility of a pattern  ? utiltiy of its sub-pattern
Parallelization to deal with hardness in mining big data

4. High Utility Pattern Mining
Problem Statement & Preliminaries
High Utility Pattern Mining
High Utility Pattern Mining
What products purchased together have high profits?
The utility of a set of products = the profits of the products in transactions containing them and depending on quantity and price/cost
FP: What products are frequently purchased together?
Shopping Transactions
Utility table
Tid
Items t1
b:1, c:2, d:1, g:1 t2
a:4, b:1 c:3, d:1,e:1 t3
a:4, c:2, d:1 t4
c:2, e:1,f:1
... I U a 1 b 2 c d 5
...

5. Well-known Sequential Mining Algorithms
Problem Statement & Preliminaries
Well-known Sequential Algorithms
Well-known Sequential Mining Algorithms
Algorithm
References
Search Strategy
Candidates
Pruning Strategy
TwoPhase
[1] KDD
Breadth (Apriori)
With
TWU
CTU-PROL
[3] PAKDD
IHUP
[5] TKDE
Depth (FP-Growth)
UPGrowth
[6] KDD, TKDE
D2HUP
[7] ICDM, TKDE
Depth (OP)
Without
Tight bound
HUI-Miner
[8] CIKM
Depth (Eclat)
Tight bounds

6. Distributed Computing Frameworks [9,10]
Problem Statement & Preliminaries
Spark / MapReduce Framework
Distributed Computing Frameworks [9,10]
Data are distributed over a cluster
One split on one node
Represented as <key, value> pairs: input, output, and interim results
Processing by a series of jobs
Job is dispatched to where a data split reside, and executed in parallel
Job is defined by a mapper and a reducer, and executed in two phases
Resilient Dynamic Dataset (RDD): Memory based
Transformations / Actions on RDD
Master
Slaves
Cluster of servers (nodes)

7. Breadth-First Search, Improved Utility Lists
Our Mining Approach
Breadth-First Search, Improved Utility Lists
Our Mining Approach
Breadth-First Search
adapting HUI-Miner derived from Eclat , which is Depth-First
Improved vertical data structure - UtilityList
Ordering items, e, c, b, a, d, in ascending transaction utilities
 {e}, UL({e}) 
 {b}, UL({b}) 

8. Our Mining Approach (cont)
Join Utility Lists
Our Mining Approach (cont)
Mining high utility patterns by joining UtilityLists
two k-patterns
(k+1)-pattern
 {e,b}, UL({e,b}) 
 {e,a}, UL({e,a}) 
3.2 Enabling Our Opportunistic Vertical Mining
 {e,b,a}, UL({e,b,a}) 

9. Phps: Parallel high utility pattern mining based on Spark
New Parallel Algorithm Based on Spark
Three phases
Phps: Parallel high utility pattern mining based on Spark
 i, (u(i,tid), u(t,tid) )  I
 i, twu(i) )  II
 i, (tid, iutil, rutil) 
 i, List(tid, iutil, rutil,piutil) 
 i, (List(,,,), iutilSum, rutilSum) 
III
 Pk,UL(Pk) 
 Pk, List(,,,) 
 Pk-2, (Pk-1, UL(Pk-1)) 
 Pk-1,UL(Pk-1) 

10. Experimental Evaluation
2 algorithms
Phps our algorithm
PhpMR the competitor
4 datasets
Dataset
#Items
#Trans.
Trans Ave Len
Chess 76
3,196 37
WebView-1
497
59,602
2.5
T10DI6N1KD1M
1,000
933,493 10
Chainstore
46,086
1,112,949
7.2

11. Running time with changing minUtil
Experimental Evaluation
Running time with changing minUtil
Running time with changing minUtil

12. Running time with each iteration
Experimental Evaluation
Running time with each iteration
Running time with each iteration

13. Conclusion Future Work
Conclusion and Future Work
Conclusion
Phps: a parallel Eclat-like algorithm based on Spark
An improved vertical data structure
A three-phase parallel mining framework
An efficient algorithm
Future Work
Hybrid Search : BF + DF
More Pruning in Phase I (filtering irrelevant items)
Algorithms parallelizing D2HUP
Algorithms on new parallel programming frameworks

14. References
[1] Y. Liu, W. Liao, and A. Choudhary. A fast high utility itemsets mining algorithm. In Proceedings of the Utility-Based Data MiningWorkshop in conjunction with the 11th ACM SIGKDD [C], 2005, p [2] Y.-C. Li, J.-S. Yeh, and C.-C. Chang. Isolated items discarding strategy for discovering high utility itemsets [J]. Data & Knowledge Engineering, 2008, 64(1): [3] A. Erwin, R. P. Gopalan, and N. R. Achuthan. Efficient mining of high utility itemsets from large datasets [A]. In Proceedings of PAKDD 2008 [C], 2008, p [4] J. W. Han, J. Pei, Y. W. Yin, et al. Mining Frequent Patterns without Candidate Generation. In Proceedings of the 2000 ACMSIGMOD International Conference on Management of Data, 2000, p1-12. [5] C. F. Ahmed, S. K. Tanbeer, B.-S. Jeong, et al. Efficient tree structures for high utility pattern mining in incremental databases[J]. In IEEE Transactions on Knowledge and Data Engineering, 2009, p [6] V. S. Tseng, C.-W. Wu, B.-E. Shie, et al. UP-Growth: an efficient algorithm for high utility itemset mining [A]. In Proceedings of the 16th ACM SIGKDD [C], 2010, p [7] I J. Liu, K. Wang, and B. Fung. Direct Discovery of High Utility temsets without Candidate Generation. In IEEE 12th International Conference on Data Mining, 2012, p [8] M. Liu, J. Qu. Mining high utility itemsets without candidate generation. In Proceedings of CIKM 2012, 2012, p [9] Matei Zaharia. An architecture for fast and general data processing on large clusters. Technical Report No. UCB/EECS , University of California at Berkeley. [10] Jeffrey Dean, Sanjay Ghemawat. MapReduce: Simplified dataprocessing on large clusters. In OSDI, 2004, p

15. Thank You ! Questions ?
Gracias ! Pregunta?

16. IEEE DSC IEEE International Conference on Data Science in Cyberspace BDMC BIG DATA MINING FOR CYBERSPACE 
23 June, 2019
8:30 - 9:30
Workshop Chair: Zhaoquan Gu and Jing Qiu