The identification of frequent patterns plays a key role in mining association rules. FP-growth is a fundamental algorithm for frequent pattern mining. It employs a prefix tree structure (FP-Tree) and a recursive mining process to discover frequent patterns. However, the performance of FP-growth is closely related to the total number of recursive calls, which leads to poor performance when multiple conditional FP-trees are required to be constructed. This paper proposes highly compressed FP-tree (HCFP-tree). This increases prefix sharing and reduces the number of nodes in the prefix tree. Based on HCFP-tree, we design a new algorithm called HCFP-growth. This algorithm greatly reduces the number of recursive calls required to mine full frequent patterns. Experiments conducted on various types of datasets demonstrate that HCFP-growth is always among the fastest algorithms. It also consumes the least memory in many cases, and its memory consumption is comparable to that of existing algorithms in other cases.
LiuB.HsuW. and MaY., Integrating classification and association rule mining, in: Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, 1998.
2.
AgrawalR.GehrkeJ.GunopulosD. and RaghavanP., Automatic subspace clustering of high dimensional data, Data Mining & Knowledge Discovery11 (2005), 5–33.
3.
RuggieriS., Frequent regular itemset mining, in: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, Dc, Usa, July, 2010, pp. 263–272.
4.
NaharJ.ImamT.TickleK.S. and ChenY.-P.P., Association rule mining to detect factors which contribute to heart disease in males and females, Expert Systems with Applications40 (2013), 1086–1093.
5.
LiH.-F., A sliding window method for finding top-k path traversal patterns over streaming Web click-sequences, Expert Systems with Applications36 (2009), 4382–4386.
6.
WengC.-H., Revenue prediction by mining frequent itemsets with customer analysis, Engineering Applications of Artificial Intelligence63 (2017), 85–97.
7.
ChenY.C.PengW.C. and LeeS.Y., CEMiner – An Efficient Algorithm for Mining Closed Patterns from Time Interval-Based Data, in: IEEE International Conference on Data Mining, 2011, pp. 121–130.
8.
AgrawalR. and SrikantR., Fast algorithms for mining association rules in large databases, in: Proceedings of the 20th International Conference on Very Large Data Bases, Sep 12–15, 1994, Santiago, Chile, 1994, pp. 487–487.
9.
JiaweiH.JianP. and YiwenY., Mining frequent patterns without candidate generation, Sigmod Record29 (2000), 1–12.
10.
LeungC.K.S. and MacKinnonR.K., Fast Algorithms for Frequent Itemset Mining from Uncertain Data, in: 2014 IEEE International Conference on Data Mining, 2014, pp. 893–898.
11.
LeungK.S.HaoB. and JiangF., Constrained frequent itemset mining from uncertain data streams, in: IEEE International Conference on Data Engineering Workshops, 2010, pp. 120–127.
12.
YunU. and LeggettJ.J., WIP: Mining Weighted Interesting Patterns with a strong weight and/or support affinity, in: Siam International Conference on Data Mining, April 20–22, 2006, Bethesda, Md, Usa, 2006, pp. 3477–3499.
13.
NguyenH.VoB.NguyenM. and PedryczW., An efficient algorithm for mining frequent weighted itemsets using interval word segments, Applied Intelligence45 (2016), 1–13.
14.
CaglieroL. and GarzaP., Infrequent weighted itemset mining using frequent pattern growth, IEEE Transactions on Knowledge & Data Engineering26 (2014), 903–915.
15.
JiS.WangX.ZongY. and GaoX., Mining Top-K Frequent Closed Patterns from Gene Expression Data, in: IEEE International Conference on Data Mining Workshop, 2014, pp. 732–739.
16.
DamT.-L.LiK.Fournier-VigerP. and DuongQ.-H., An efficient algorithm for mining top-rank-k frequent patterns, Applied Intelligence45 (2016), 96–111.
17.
GanW.LinJ.C.-W.Fournier-VigerP.ChaoH.-C. and ZhanJ., Mining of frequent patterns with multiple minimum supports, Engineering Applications of Artificial Intelligence60 (2017), 83–96.
18.
ChenJ. and XiaoK., BISC: a bitmap itemset support counting approach for efficient frequent itemset mining, Acm Transactions on Knowledge Discovery from Data4 (2010), 1–37.
19.
DengZ.H., DiffNodesets: an efficient structure for fast mining frequent itemsets, Computer Science41 (2015), 214–223.
20.
LinK.C.LiaoI.E. and ChenZ.S., An improved frequent pattern growth method for mining association rules, Expert Systems with Applications38 (2011), 5154–5161.
21.
PyunG.YunU. and RyuK.H., Efficient frequent pattern mining based on Linear Prefix tree, Knowledge-Based Systems55 (2014), 125–139.
22.
AlghyalineS.HsiehJ.W. and LaiJ.Z.C., Efficiently mining frequent itemsets in transactional databases, Journal of Marine Science & Technology24 (2016), 184–191.
23.
GatuhaG. and JiangT., Smart frequent itemsets mining algorithm based on FP-tree and DIFFset data structures, Turkish Journal of Electrical Engineering & Computer Sciences25 (2017), 2096–2107.
24.
DengZ.H. and LvS.L., PrePost +: An efficient N-lists-based algorithm for mining frequent itemsets via Children-Parent Equivalence pruning, Expert Systems with Applications42 (2015), 5424–5432.
25.
VoB.LeT.CoenenF. and HongT.-P., Mining frequent itemsets using the N-list and subsume concepts, International Journal of Machine Learning and Cybernetics7 (2016), 253–265.
26.
GiriS.Y. and PG.R., CT-PRO: A Bottom-Up Non Recursive Frequent Itemset Mining Algorithm Using Compressed FP-Tree Data Structure, in: Fimi ’04, Proceedings of the IEEE ICDM Workshop on Frequent Itemset Mining Implementations, Brighton, Uk, November, 2004.
27.
RáczB., nonordfp: An FP-growth variation without rebuilding the FP-tree, in: Fimi ’04, Proceedings of the IEEE ICDM Workshop on Frequent Itemset Mining Implementations, Brighton, Uk, November, 2008.
28.
BurdickD.CalimlimM. and GehrkeJ., Mafia: A maximal frequent itemset algorithm for transactional databases, in: Data Engineering, 2001. Proceedings. 17th International Conference on, 2001, pp. 443–452.
