Mining Sets of Patterns

Mining Sets of Patterns a tutorial at ECMLPKDD’10, 20 September 2010, Barcelona by Bj¨orn Bringmann, Siegfried Nijssen, Nikolaj Tatti, Jilles Vreeken and Albrecht Zimmermann Reference Overview This document provides an overview of the methods and approaches we cover in our tutorial. Please find the slides for the presentation, as well as the most recent version of this document at: http://www.cs.kuleuven.be/conference/msop/

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Introduction — Mining (Sets of) Patterns

1.1 Pattern Mining Definitions of patterns (Hand et al, 2002) Apriori (Agrawal et al, 1993),(Agrawal and Srikant, 1994), (Agrawal et al, 1996) FP-Growth (Han et al, 2004),(Han and Pei, 2000) Frequent Itemset Mining implementation and data repository (Goethals and Zaki, 2003) Perspectives (Han et al, 2007),(Zaki and Orihara, 1998) Measures for association rules (Omiecinski, 2003), (Wu et al, 2007), (Tan et al, 2004), (Silverstein et al, 1998) 1.2 Constraint-based Mining (Anti-)monotonic constraints (Mannila and Toivonen, 1996; Han et al, 1999; Pei et al, 2001; Bucila et al, 2003; Bonchi and Lucchese, 2007; De Raedt et al, 2008; Bonchi et al, 2009) Hierarchies of items (generalized itemsets) (Srikant and Agrawal, 1995) Unexpected itemsets (Jaroszewicz and Simovici, 2004; Jaroszewicz and Scheffer, 2005; Sun et al, 2009) 1.3 Condensed Representations Multiple uses of frequent sets and condensed representations (Mannila and Toivonen, 1996) Closed Frequent Itemsets (Pasquier et al, 1999) Maximal Frequent Itemsets (Bayardo, 1998) Non-Derivable Itemsets (Calders and Goethals, 2002) 1.4 Supervised Pattern Mining Emerging patterns (Dong and Li, 1999, 2005) Subgroups (Kl¨osgen, 1996; Wrobel, 1997; Kavsek et al, 2003; Grosskreutz et al, 2008) Contrast sets (Bay and Pazzani, 2001) Correlating patterns (Morishita and Sese, 2000) Discriminative patterns (Cheng et al, 2007) 1

(Interesting) rules (Bayardo Jr. and Agrawal, 1999; Morimoto et al, 1998; Webb, 1995, 2005) Studies of relationships between approaches (Novak et al, 2009; Nijssen et al, 2009; Nijssen and Kok, 2005) Supervised sequence patterns (Bringmann et al, 2006; Hirao et al, 2003) Supervised tree patterns (Zimmermann and Bringmann, 2005; Hashimoto et al, 2008) Supervised graph patterns (Bringmann et al, 2006; Geamsakul et al, 2003; Yan et al, 2008; Nowozin et al, 2007a) Supervised patterns on itemsets, with FP trees (Han et al, 2004; Cheng et al, 2008b; Atzm¨uller and Puppe, 2006) Supervised patterns on itemsets, with BDDs (Loekito and Bailey, 2006) Supervised patterns on itemsets, with CP (Nijssen et al, 2009) False positives in supervised pattern mining (Bay and Pazzani, 2001; Webb, 2007)

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Mining Sets of Patterns — Unsupervised

2.1 Deviation-based Methods 2.1.1 Static Lift = Interest = Strength (Brin et al, 1997; Dhar and Tuzhilin, 1993) related to above (Aggarwal and Yu, 1998) The Pattern Ranking Problem (Mielik¨ainen and Mannila, 2003) Bayesian Network-based model (Jaroszewicz and Scheffer, 2005; Jaroszewicz and Simovici, 2004) Exponential model-based model (Gallo et al, 2007) Randomization-based methods, binary (Gionis et al, 2006), numeric (Ojala et al, 2009) Maximum Entropy-based methods (Jaroszewicz and Simovici, 2002; Tatti, 2007; Meo, 2002), Magnum Opus, pattern mining and statistical testing (Webb, 2007) 2.1.2 Dynamic Probabilistic Summaries (Wang and Parthasarathy, 2006) Swap-Randomization (Hanhij¨arvi et al, 2009)

2.2 Description-based Methods Tiling, set-cover like (Geerts et al, 2004) Nested-tiling (Gionis et al, 2004; Sepp¨anen and Mannila, 2004) Binary matrix factorization (Miettinen et al, 2008) Junction-tree based data description (Tatti and Heikinheimo, 2008) Constraint-based Pattern Set Mining (De Raedt and Zimmermann, 2007) 2.2.1 Compression-related methods K RIMP, itemset selection by lossless compression of the data (Siebes et al, 2006) LESS, low-entropy set selection (Heikinheimo et al, 2009) PACK, itemset selection by compressing the data with decision trees (Tatti and Vreeken, 2008) 2

Profiles, lossy compression of itemset data (Yan et al, 2005) R-K RIMP, K RIMP-like selection for relational itemsets (Koopman and Siebes, 2008) RDB-K RIMP, K RIMP-like selection of multi-relational itemsets (Koopman and Siebes, 2009) Information-theoretic noisy tiles (Kontonasios and De Bie, 2010) 2.3 Presence-based Methods Most-Informative k-Itemsets (Knobbe and Ho, 2006a) Exhaustive presence-based selection, Pattern Teams (Knobbe and Ho, 2006b) Greedy presence-based selection (Bringmann and Zimmermann, 2007) Feature Selection by MDL (Pfahringer, 1995) Constraint-based Pattern Set Mining (De Raedt and Zimmermann, 2007) 2.4 Pattern Clustering-based Redundancy aware top-k patterns (Xin et al, 2006) Semantically meaningful patterns (Yuan et al, 2007) Sampling representative patterns (Hasan et al, 2007) Mining representative subgraphs (Zhang et al, 2009) 2.5 Using Pattern Sets Difference measurement and characterisation (Vreeken et al, 2007) Missing Value Estimation (Vreeken and Siebes, 2008) Clustering (Van Leeuwen et al, 2009; Wang et al, 1999; Seeland et al, 2010; Fung et al, 2003)

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Mining Sets of Patterns — Supervised

3.1 Scoring Pattern Sets Model Independently Class correspondences (Thoma et al, 2009) Class-correlated dispersion (R¨uckert and Kramer, 2007) 3.2 Post-processing Patterns into Models (Heuristic) Processing patterns greedily (Dong et al, 1999; Wang and Karypis, 2005; Li et al, 2001a; Zaki and Aggarwal, 2003; Ramamohanarao and Fan, 2007; Arunasalam and Chawla, 2006) Processing patterns in fixed order (Liu et al, 1998; Li et al, 2001b; Zimmermann and Bringmann, 2005; van Leeuwen et al, 2006; Bouzouita et al, 2006) No processing; only pattern constraints (Zhang et al, 2000; Kramer and De Raedt, 2001; Bringmann et al, 2006) Bayesian classification (Meretakis et al, 2000; Meretakis and W¨uthrich, 1999)

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3.3 Post-processing Patterns (Optimal) Pattern teams (Knobbe and Ho, 2006b) Aposteriori (De Raedt and Zimmermann, 2007) Decision trees (DL8) (Nijssen and Fromont, 2007) 3.4 Iterative Mining Optimized on parts of data, applied on part of the data (Bringmann and Zimmermann, 2005) Optimized on all data, applied on all data (Thoma et al, 2009) Optimized on parts of data, applied on all data (Zimmermann et al, 2010) Patterns as tests in decision trees (Tree2 , local) (Bringmann and Zimmermann, 2005; Geamsakul et al, 2003; Cheng et al, 2008a) Patterns as weak learners in boosting (Nowozin et al, 2007a,b) Patterns as attributes in regression (Saigo et al, 2008; Tsuda, 2007) Instance-based (Veloso et al, 2006, 2007; Li et al, 2000) 3.5 Dedicated Experimental Comparisons Molecules (Deshpande et al, 2005; Wale et al, 2008; Bringmann et al, 2006) Greedy vs complete on attribute-value data (Janssen and F¨urnkranz, 2009, 2010)

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