Efficient Infrequent Itemset Mining Using Depth-First and Top-Down Lattice Traversal
Frequent itemset mining is substantially studied in the past decades. In varies practical applications, frequent patterns are obvious and expected, while really interesting information might hide in obscure rarity. However, existing rare pattern mining approaches are time and memory consuming due to their apriori based candidate generation step. In this paper, we propose an efficient rare pattern extraction algorithm, which is capable of extracting the complete set of rare patterns using a top-down traversal strategy. A negative item tree is employed to accelerate the mining process. Pattern growth paradigm is used and therefore avoids expensive candidate generation.
- 1.Adda, M., Wu, L., Feng, Y.: Rare itemset mining. In: Sixth International Conference on Machine Learning and Applications 2007, ICMLA 2007, pp. 73–80. IEEE (2007)Google Scholar
- 2.Agrawal, R., Srikant, R., et al.: Fast algorithms for mining association rules. In: Proceedings 20th International Conference Very Large Data Bases, VLDB, vol. 1215, pp. 487–499 (1994)Google Scholar
- 3.Gupta, A., Mittal, A., Bhattacharya, A.: Minimally infrequent itemset mining using pattern-growth paradigm and residual trees. In: Proceedings of the 17th International Conference on Management of Data, p. 13 (2011)Google Scholar
- 5.Hoque, N., Nath, B., Bhattacharyya, D.: An efficient approach on rare association rule mining. In: Bansal, J., Singh, P., Deep, K., Pant, M., Nagar, A. (eds.) Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012), pp. 193–203. Springer, Heidelberg (2013). https://doi.org/10.1007/978-81-322-1038-2_17CrossRefGoogle Scholar
- 6.Koh, Y.S., Ravana, S.D.: Unsupervised rare pattern mining: a survey. ACM Trans. Knowl. Discov. Data (TKDD) 10(4), 45 (2016)Google Scholar
- 7.Szathmary, L., Napoli, A., Valtchev, P.: Towards rare itemset mining. In: 19th IEEE International Conference on Tools with Artificial Intelligence 2007, ICTAI 2007, vol. 1, pp. 305–312. IEEE (2007)Google Scholar