Mustergraphen: Klassifikation von multivariaten Zeitreihen auf Basis von Intervallsequenzen

[1]  Stefano Ferilli,et al.  A Relational Approach to Sensor Network Data Mining , 2011, Information Retrieval and Mining in Distributed Environments.

[2]  Marcel Worring,et al.  Multimedia event-based video indexing using time intervals , 2005, IEEE Transactions on Multimedia.

[3]  Weixiong Zhang,et al.  State-Space Search , 1999, Springer New York.

[4]  Neha Mehra,et al.  Survey on Multiclass Classification Methods , 2013 .

[5]  Dan Gâlea,et al.  Multicriteria Decision Making Based on Fuzzy Relations , 2008 .

[6]  Robert S. Boyer,et al.  A fast string searching algorithm , 1977, CACM.

[7]  Heikki Mannila,et al.  Discovery of Frequent Episodes in Event Sequences , 1997, Data Mining and Knowledge Discovery.

[8]  Alberto Maria Segre,et al.  Programs for Machine Learning , 1994 .

[9]  Fabian Mörchen,et al.  Time Series Knowledge Mining , 2006 .

[10]  Peter Weiner,et al.  Linear Pattern Matching Algorithms , 1973, SWAT.

[11]  Fabian Mörchen,et al.  Optimizing time series discretization for knowledge discovery , 2005, KDD '05.

[12]  Jianyong Wang,et al.  Mining sequential patterns by pattern-growth: the PrefixSpan approach , 2004, IEEE Transactions on Knowledge and Data Engineering.

[13]  Donald E. Knuth,et al.  Fast Pattern Matching in Strings , 1977, SIAM J. Comput..

[14]  Michael R. Berthold,et al.  Enriching Multivariate Temporal Patterns with Context Information to Support Classification , 2013 .

[15]  Esko Ukkonen,et al.  On-line construction of suffix trees , 1995, Algorithmica.

[16]  James E. Kelley,et al.  Critical-path planning and scheduling , 1899, IRE-AIEE-ACM '59 (Eastern).

[17]  John F. Roddick,et al.  Linear temporal sequences and their interpretation using midpoint relationships , 2005, IEEE Transactions on Knowledge and Data Engineering.

[18]  Rajeev Motwani,et al.  Einführung in die Automatentheorie, formale Sprachen und Komplexitätstheorie (2. Aufl.) , 1990, Internationale Computer-Bibliothek.

[19]  Eamonn J. Keogh,et al.  Segmenting Time Series: A Survey and Novel Approach , 2002 .

[20]  Rajeev Alur,et al.  A Theory of Timed Automata , 1994, Theor. Comput. Sci..

[21]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.

[22]  Dmitriy Fradkin,et al.  Margin-closed frequent sequential pattern mining , 2010, UP '10.

[23]  John F. Roddick,et al.  Adding Temporal Semantics to Association Rules , 1999, PKDD.

[24]  F. Mörchen.  A better tool than Allen's relations for expressing temporal knowledge in interval data , 2006 .

[25]  Yoram Singer,et al.  Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers , 2000, J. Mach. Learn. Res..

[26]  Christian Freksa,et al.  Temporal Reasoning Based on Semi-Intervals , 1992, Artif. Intell..

[27]  Stefano Ferilli,et al.  Relational Temporal Data Mining for Wireless Sensor Networks , 2009, AI*IA.

[28]  Peter Clark,et al.  Rule Induction with CN2: Some Recent Improvements , 1991, EWSL.

[29]  Carlo Combi,et al.  Data mining with Temporal Abstractions: learning rules from time series , 2007, Data Mining and Knowledge Discovery.

[30]  P. S. Sastry,et al.  Discovering Frequent Generalized Episodes When Events Persist for Different Durations , 2007, IEEE Transactions on Knowledge and Data Engineering.

[31]  Mong-Li Lee,et al.  Mining relationships among interval-based events for classification , 2008, SIGMOD Conference.

[32]  Frank Höppner.  Discovery of Temporal Patterns. Learning Rules about the Qualitative Behaviour of Time Series , 2001, PKDD.

[33]  Stefano Ferilli,et al.  Multi-Dimensional Relational Sequence Mining , 2008, Fundam. Informaticae.

[34]  Sebastian Peter,et al.  Finding Temporal Patterns Using Constraints on (Partial) Absence, Presence and Duration , 2010, KES.

[35]  Marco Aiello,et al.  Document understanding for a broad class of documents , 2002, Int. J. Document Anal. Recognit..

[36]  Fosca Giannotti,et al.  Temporal mining for interactive workflow data analysis , 2009, KDD.

[37]  Wil M. P. van der Aalst,et al.  Rediscovering workflow models from event-based data using little thumb , 2003, Integr. Comput. Aided Eng..

[38]  Boudewijn F. van Dongen,et al.  Discovering Petri Nets from Event Logs , 2013, Trans. Petri Nets Other Model. Concurr..

[39]  Boudewijn F. van Dongen,et al.  Process Mining: Overview and Outlook of Petri Net Discovery Algorithms , 2009, Trans. Petri Nets Other Model. Concurr..

[40]  Edward M. McCreight,et al.  A Space-Economical Suffix Tree Construction Algorithm , 1976, JACM.

[41]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[42]  Kien A. Hua,et al.  Mining Interval Time Series , 1999, DaWaK.

[43]  Eamonn J. Keogh,et al.  Time series shapelets: a new primitive for data mining , 2009, KDD.

[44]  Thomas G. Dietterich,et al.  Solving Multiclass Learning Problems via Error-Correcting Output Codes , 1994, J. Artif. Intell. Res..

[45]  Milos Hauskrecht,et al.  A Pattern Mining Approach for Classifying Multivariate Temporal Data , 2011, 2011 IEEE International Conference on Bioinformatics and Biomedicine.

[46]  Dmitriy Fradkin,et al.  Robust Mining of Time Intervals with Semi-interval Partial Order Patterns , 2010, SDM.

[47]  Ryan M. Rifkin,et al.  In Defense of One-Vs-All Classification , 2004, J. Mach. Learn. Res..

[48]  Wil M. P. van der Aalst,et al.  Process mining: a research agenda , 2004, Comput. Ind..

[49]  Milos Hauskrecht,et al.  Mining recent temporal patterns for event detection in multivariate time series data , 2012, KDD.

[50]  Ada Wai-Chee Fu,et al.  Discovering Temporal Patterns for Interval-Based Events , 2000, DaWaK.

[51]  Boudewijn F. van Dongen,et al.  Discovering Workflow Performance Models from Timed Logs , 2002, EDCIS.

[52]  Michael R. Berthold,et al.  Pattern graphs: A knowledge-based tool for multivariate temporal pattern retrieval , 2012, 2012 6th IEEE International Conference Intelligent Systems.

[53]  Yaw-Ling Lin,et al.  Hybrid Temporal Pattern Mining with Time Grain on Stock Index , 2011, 2011 Fifth International Conference on Genetic and Evolutionary Computing.

[54]  Tao Jiang,et al.  On the Complexity of Multiple Sequence Alignment , 1994, J. Comput. Biol..

[55]  Massimiliano Giacomin,et al.  A Fuzzy Extension of Allen's Interval Algebra , 1999, AI*IA.

[56]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[57]  Richard M. Karp,et al.  Efficient Randomized Pattern-Matching Algorithms , 1987, IBM J. Res. Dev..

[58]  Howard J. Hamilton,et al.  Interestingness measures for data mining: A survey , 2006, CSUR.

[59]  G. Guimarães,et al.  A Symbolic Representation for Patterns in Time Series Using Definitive Clause Grammars , 1997 .

[60]  Paul R. Cohen,et al.  Learning effects of robot actions using temporal associations , 2002, Proceedings 2nd International Conference on Development and Learning. ICDL 2002.

[61]  Li Wei,et al.  Experiencing SAX: a novel symbolic representation of time series , 2007, Data Mining and Knowledge Discovery.

[62]  Yen-Liang Chen,et al.  Discovering hybrid temporal patterns from sequences consisting of point- and interval-based events , 2009, Data Knowl. Eng..

[63]  Johannes Fürnkranz,et al.  Round Robin Classification , 2002, J. Mach. Learn. Res..

[64]  Kishan G. Mehrotra,et al.  Efficient classification for multiclass problems using modular neural networks , 1995, IEEE Trans. Neural Networks.

[65]  Frank Klawonn,et al.  Guide to Intelligent Data Analysis - How to Intelligently Make Sense of Real Data , 2010, Texts in Computer Science.

[66]  M. Hemalatha.  Time ontology with Reference Event based Temporal Relations (RETR) , 2012 .

[67]  James F. Allen.  Maintaining knowledge about temporal intervals , 1983, CACM.

[68]  Wolfgang Reisig,et al.  Lectures on Petri Nets I: Basic Models , 1996, Lecture Notes in Computer Science.

[69]  Guido Schimm.  Generic Linear Business Process Modeling , 2000, ER.

[70]  Fabian Mörchen,et al.  Efficient mining of understandable patterns from multivariate interval time series , 2007, Data Mining and Knowledge Discovery.

[71]  Andreas D. Lattner,et al.  Sequential Pattern Mining for Situation and Behavior Prediction in Simulated Robotic Soccer , 2005, RoboCup.

[72]  Robert Tibshirani,et al.  Classification by Pairwise Coupling , 1997, NIPS.

[73]  G. Aghila,et al.  Temporal pattern mining and reasoning using Reference Event based Temporal Relations (RETR) , 2011 .

[74]  Frank Klawonn,et al.  Finding informative rules in interval sequences , 2001, Intell. Data Anal..

[75]  Wil M. P. van der Aalst,et al.  Workflow mining: discovering process models from event logs , 2004, IEEE Transactions on Knowledge and Data Engineering.

[76]  Boris Motik,et al.  A Fuzzy Model for Representing Uncertain, Subjective, and Vague Temporal Knowledge in Ontologies , 2003, OTM.

[77]  Chih-Ping Wei,et al.  Discovery of temporal patterns from process instances , 2004, Comput. Ind..

[78]  Michael R. Berthold,et al.  Learning Pattern Graphs for Multivariate Temporal Pattern Retrieval , 2012, IDA.

[79]  Wil M. P. van der Aalst,et al.  Discovering colored Petri nets from event logs , 2007, International Journal on Software Tools for Technology Transfer.

[80]  Jian Pei,et al.  Mining Access Patterns Efficiently from Web Logs , 2000, PAKDD.

[81]  Wil M. P. van der Aalst,et al.  The Application of Petri Nets to Workflow Management , 1998, J. Circuits Syst. Comput..

[82]  Paul R. Cohen,et al.  Fluent Learning: Elucidating the Structure of Episodes , 2001, IDA.

[83]  Ramakrishnan Srikant,et al.  Mining Sequential Patterns: Generalizations and Performance Improvements , 1996, EDBT.

[84]  Hans Jürgen Ohlbach.  Relations between fuzzy time intervals , 2004, Proceedings. 11th International Symposium on Temporal Representation and Reasoning, 2004. TIME 2004..

[85]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[86]  Heikki Mannila,et al.  Rule Discovery from Time Series , 1998, KDD.

[87]  Padhraic Smyth,et al.  An Information Theoretic Approach to Rule Induction from Databases , 1992, IEEE Trans. Knowl. Data Eng..

[88]  Jiawei Han,et al.  BIDE: efficient mining of frequent closed sequences , 2004, Proceedings. 20th International Conference on Data Engineering.

[89]  Frank Höppner,et al.  Classification Based on the Trace of Variables over Time , 2007, IDEAL.

[90]  J. Ross Quinlan,et al.  Improved Use of Continuous Attributes in C4.5 , 1996, J. Artif. Intell. Res..

[91]  Eamonn J. Keogh,et al.  Clustering of time-series subsequences is meaningless: implications for previous and future research , 2004, Knowledge and Information Systems.

[92]  Ming-Syan Chen,et al.  Mining Sequential Alarm Patterns in a Telecommunication Database , 2001, Databases in Telecommunications.

[93]  Ramakrishnan Srikant,et al.  Mining sequential patterns , 1995, Proceedings of the Eleventh International Conference on Data Engineering.

[94]  Cees Witteveen,et al.  Efficiently learning simple timed automata , 2008 .

[95]  Michael R. Berthold,et al.  Pattern Graphs: Combining Multivariate Time Series and Labelled Interval Sequences for Classification , 2013, SGAI Conf..

[96]  Fabian Mörchen,et al.  Unsupervised pattern mining from symbolic temporal data , 2007, SKDD.

[97]  S C Kleene,et al.  Representation of Events in Nerve Nets and Finite Automata , 1951 .

[98]  Marc B. Vilain,et al.  A System for Reasoning About Time , 1982, AAAI.

[99]  Sebastian Peter,et al.  Temporal interval pattern languages to characterize time flow , 2014, WIREs Data Mining Knowl. Discov..

[100]  John F. Roddick,et al.  Sequential pattern mining -- approaches and algorithms , 2013, CSUR.

[101]  Jaak Vilo.  Discovering Frequent Patterns from Strings , 1998 .

[102]  Suh-Yin Lee,et al.  An efficient algorithm for mining time interval-based patterns in large database , 2010, CIKM.

[103]  James E. Kelley,et al.  Critical-Path Planning and Scheduling: Mathematical Basis , 1961 .

[104]  Weixiong Zhang.  State-space search - algorithms, complexity, extensions, and applications , 1999 .

[105]  Mohammed J. Zaki,et al.  SPADE: An Efficient Algorithm for Mining Frequent Sequences , 2004, Machine Learning.