, We start from the least frequent product, chocolate. The skyline is {{Chocolate}}. b.Adds Orange juice. It has the same support as {Chocolate} but is larger so it replaces {Chocolate} in the skyline. The skyline is {{Chocolate, Orange juice}}. c.Same as b., but with milk. The skyline is {{Chocolate, Orange juice, Milk}}. d.Same as c., but with bread. The skyline is {{Chocolate, Orange juice, Milk, Bread}}. e. {Chocolate, Orange juice, Bread} is dominated by {Chocolate, Orange juice, Milk, Bread} so the skyline remains. f. Same as e. g. {Bread} is not dominated by {Chocolate, Orange juice, Milk, Bread} so it is included in the skyline. {Bread} does not dominate {Chocolate, Orange juice, Milk, Bread}, so {Chocolate, Orange juice, Milk, Bread} remains in the skyline
Approximating a collection of frequent sets, Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, pp.12-19, 2004. ,
DOI : 10.1145/1014052.1014057
Mining Association Rules Between Sets of Items in Large Databases, Proc. 17th Int. Conf. on Management of Data, pp.207-216, 1993. ,
Fast algorithms for mining association rules, Proc. 20th int. conf. very large data bases, VLDB, vol.1215, pp.487-499, 1994. ,
Mining sequential patterns, Proc. 11th Int. Conf. on Data Engineering, pp.3-14, 1995. ,
Sequential pattern mining using a bitmap representation, Proc. 8th Conf. on Knowledge Discovery and Data mining, pp.429-435, 2002. ,
Dynamic programming, Courier Corporation, 2013. ,
On the approximation of curves by line segments using dynamic programming, Communications of the ACM, vol.4, p.284, 1961. ,
Finding Segmentations of Sequences, Inductive Databases and Constraint-Based Data Mining, pp.177-197, 2010. ,
Natural language processing with Python: analyzing text with the natural language toolkit, 2009. ,
Latent dirichlet allocation, Journal of machine Learning research, vol.3, pp.993-1022, 2003. ,
Anytime discovery of a diverse set of patterns with Monte Carlo tree search, Data Mining and Knowledge Discovery, vol.32, pp.604-650, 2018. ,
URL : https://hal.archives-ouvertes.fr/hal-01418663
Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting, European Journal of Operational Research, vol.164, pp.252-268, 2005. ,
, Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2015, pp.978-981, 2015.
Efficiently Summarising Event Sequences with Rich Interleaving Patterns, Proc. of the SIAM International Conference on Data Mining, pp.795-803, 2017. ,
Discovering unbounded episodes in sequential data, European Conference on Principles of Data Mining and Knowledge Discovery, pp.83-94, 2003. ,
Debugging embedded multimedia application traces through periodic pattern mining, Proc. 12th Int. Conf. on Embedded Software, pp.13-22, 2012. ,
Constraint-Based Pattern Set Mining, Proceedings of the Seventh SIAM International Conference on Data Mining, Minneapolis, pp.237-248, 2007. ,
Exploring the Political Agenda of the European Parliament Using a Dynamic Topic Modeling Approach, Political Analysis, vol.25, pp.77-94, 2017. ,
k-Pattern set mining under constraints, Knowledge and Data Engineering, vol.25, pp.402-418, 2013. ,
Efficient mining of temporally annotated sequences, Proceedings of the 2006 SIAM International Conference on Data Mining, SIAM, pp.348-359, 2006. ,
The minimum description length principle, 2007. ,
Mining top-k frequent closed patterns without minimum support, Proc. Int. Conf. on Data Mining (ICDM), pp.211-218, 2002. ,
Efficient mining of partial periodic patterns in time series database, Proceedings., 15th International Conference on, pp.106-115, 1999. ,
Unimodal segmentation of sequences, Data Mining, 2004. ICDM'04. Fourth IEEE International Conference on, pp.106-113, 2004. ,
DOI : 10.1109/icdm.2004.10109
Mining frequent patterns without candidate generation, ACM SIGMOD Record, vol.29, pp.1-12, 2000. ,
DOI : 10.1145/335191.335372
Diversity-driven widening, International Symposium on Intelligent Data Analysis, pp.223-236, 2013. ,
Switching barriers and repurchase intentions in services, Journal of retailing, vol.76, pp.259-274, 2000. ,
Mining Compressing Sequential Patterns, Proceedings of the Twelfth SIAM International Conference on Data Mining, pp.319-330, 2012. ,
CP-Miner: Finding copy-paste and related bugs in largescale software code, IEEE Transactions on software Engineering, vol.32, pp.176-192, 2006. ,
Pfp: parallel fp-growth for query recommendation, Proceedings of the 2008 ACM conference on Recommender systems, pp.107-114, 2008. ,
Diverse subgroup set discovery, Data Mining and Knowledge Discovery, vol.25, pp.208-242, 2012. ,
Classification of software behaviors for failure detection: a discriminative pattern mining approach, Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pp.557-566, 2009. ,
Learning the parts of objects by nonnegative matrix factorization, Nature, vol.401, pp.788-791, 1999. ,
Clustering association rules, Proceedings. 13th International Conference on, pp.220-231, 1997. ,
DOI : 10.1109/icde.1997.581756
Mining and using sets of patterns through compression, Frequent Pattern Mining, pp.165-198, 2014. ,
Frequent pattern mining for kernel trace data, Proceedings of the 2008 ACM symposium on Applied computing, pp.880-885, 2008. ,
Some methods for classification and analysis of multivariate observations, Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, vol.1, pp.281-297, 1967. ,
Wordnet: An electronic lexical database, 1998. ,
Mining partially periodic event patterns with unknown periods, Proceedings. 17th International Conference on, pp.205-214, 2001. ,
Discovery of frequent episodes in event sequences, Data mining and knowledge discovery, vol.1, pp.259-289, 1997. ,
Detecting repeats for video structuring, Multimedia Tools and Applications, vol.38, pp.233-252, 2008. ,
DOI : 10.1007/s11042-007-0180-1
URL : https://hal.archives-ouvertes.fr/inria-00001154
Cyclic association rules, Proceedings., 14th International Conference on, pp.412-421, 1998. ,
Parallel FP-growth on PC cluster, Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp.467-473, 2003. ,
DOI : 10.1007/3-540-36175-8_47
Modeling by shortest data description, Automatica, vol.14, pp.465-471, 1978. ,
DOI : 10.1016/0005-1098(78)90005-5
A universal prior for integers and estimation by minimum description length, The Annals of statistics, pp.416-431, 1983. ,
DOI : 10.1214/aos/1176346150
URL : https://doi.org/10.1214/aos/1176346150
Lexical analysis of US political speeches, Journal of Quantitative Linguistics, vol.17, pp.123-141, 2010. ,
DOI : 10.1080/09296171003643205
URL : http://doc.rero.ch/record/31091/files/Savoy_Jacques-Lexical_analysis_of_US_political_speeches-20130109.pdf
Normalized cuts and image segmentation, IEEE Transactions, vol.22, pp.888-905, 2000. ,
Mining dominant patterns in the sky, IEEE 11th International Conference on, pp.655-664, 2011. ,
DOI : 10.1109/icdm.2011.100
URL : https://hal.archives-ouvertes.fr/inria-00623566
Improving search through diversity, Proc. of the AAAI National Conf. on Artificial Intelligence, pp.1323-1328, 1994. ,
Exploring topic coherence over many models and many topics, Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp.952-961, 2012. ,
Slim: Directly Mining Descriptive Patterns, Proc. of the Twelfth SIAM International Conference on Data Mining, pp.236-247, 2012. ,
DOI : 10.1137/1.9781611972825.21
URL : https://epubs.siam.org/doi/pdf/10.1137/1.9781611972825.21
Problems and algorithms for sequence segmentations, 2006. ,
Efficient Algorithms for Sequence Segmentation, Proc. SIAM Conference on Data Mining, pp.314-325, 2006. ,
DOI : 10.1137/1.9781611972764.28
The Long and the Short of It: Summarising Event Sequences with Serial Episodes, Proc. of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol.12, pp.978-979, 2012. ,
Estimating the number of clusters in a data set via the gap statistic, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.63, pp.411-423, 2001. ,
LCM ver. 2: Efficient mining algorithms for frequent/closed/maximal itemsets, vol.126, 2004. ,
DOI : 10.1145/1133905.1133916
DBV-Miner: A Dynamic Bit-Vector approach for fast mining frequent closed itemsets, Expert Systems with Applications, vol.39, pp.7196-7206, 2012. ,
DOI : 10.1016/j.eswa.2012.01.062
Krimp: mining itemsets that compress, Data Mining and Knowledge Discovery, vol.23, pp.1384-5810, 2011. ,
DOI : 10.1007/s10618-010-0202-x
URL : https://link.springer.com/content/pdf/10.1007%2Fs10618-010-0202-x.pdf
Interesting patterns, Frequent pattern mining, pp.105-134, 2014. ,
DOI : 10.1007/978-3-319-07821-2_5
Entropy and distance of random graphs with application to structural pattern recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.5, pp.599-609, 1985. ,
CHARM: An Efficient Algorithm for Closed Itemset Mining, SDM, vol.2, pp.457-473, 2002. ,
Multidimensional topic analysis in political texts, Data & Knowledge Engineering, vol.90, pp.38-53, 2014. ,
Detecting strategic moves in hearthstone matches, Machine Learning and Data Mining for Sports Analytics Workshop of ECML/PKDD, 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-01412432
Understanding customer attrition at an individual level: a new model in grocery retail context, International Conference on Extending Database Technology (EDBT), pp.686-687, 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-01405172
Topic signatures in political campaign speeches, EMNLP 2017-Conference on Empirical Methods in Natural Language Processing, pp.2342-2347, 2017. ,
URL : https://hal.archives-ouvertes.fr/hal-01640498
Multi-plant photovoltaic energy forecasting challenge: Second place solution, Discovery Challenges co-located with European Conference on Machine Learning-Principle and Practice of Knowledge Discovery in Database, 2017. ,
URL : https://hal.archives-ouvertes.fr/hal-01639813
Purchase signatures of retail customers, Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp.110-121, 2017. ,
URL : https://hal.archives-ouvertes.fr/hal-01639795