{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,5]],"date-time":"2024-09-05T00:03:47Z","timestamp":1725494627990},"publisher-location":"Berlin, Heidelberg","reference-count":17,"publisher":"Springer Berlin Heidelberg","isbn-type":[{"type":"print","value":"9783540440369"},{"type":"electronic","value":"9783540367550"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2002]]},"DOI":"10.1007\/3-540-36755-1_26","type":"book-chapter","created":{"date-parts":[[2007,11,13]],"date-time":"2007-11-13T16:03:29Z","timestamp":1194969809000},"page":"307-318","source":"Crossref","is-referenced-by-count":1,"title":["A Multistrategy Approach to the Classification of Phases in Business Cycles"],"prefix":"10.1007","author":[{"given":"Katharina","family":"Morik","sequence":"first","affiliation":[]},{"given":"Stefan","family":"R\u00fcping","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2002,9,20]]},"reference":[{"key":"26_CR1","unstructured":"R. Agrawal and R. Srikant. Fast algorithms for mining association rules in large data bases. In Proceedings of the 20th International Conference on Very Large Data Bases (VLDB\u2019 94), pages 478\u2013499, Santiago, Chile, sep 1994."},{"key":"26_CR2","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1016\/0004-3702(84)90008-0","volume":"23","author":"J. F. Allen","year":"1984","unstructured":"J. F. Allen. Towards a general theory of action and time. Artificial Intelligence, 23:123\u2013154, 1984.","journal-title":"Artificial Intelligence"},{"key":"26_CR3","unstructured":"Marlene Amstad. Konjunkturelle Wendepunkte: Datierung und Prognose. St.Gallen, 2000."},{"key":"26_CR4","first-page":"229","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"D. J. Berndt","year":"1996","unstructured":"Donald J. Berndt and James Clifford. Finding patterns in time series: A dynamic programming approach. In Usama M. Fayyad, Gregory Piatetsky-Shapiro, Padhraic Smyth, and Ramasamy Uthurusamy, editors, Advances in Knowledge Discovery and Data Mining, chapter 3, pages 229\u2013248. AAAI Press\/The MIT Press, Menlo Park, California, 1996."},{"key":"26_CR5","unstructured":"Gautam Das, King-Ip Lin, Heikki Mannila, Gopal Renganathan, and Padhraic Smyth. Rule Discovery from Time Series. In Rakesh Agrawal, Paul E. Stolorz, and Gregory Piatetsky-Shapiro, editors, Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (KDD-98), pages 16\u201322, Ney York City, 1998. AAAI Press."},{"volume-title":"Arbeitsbericht 2000","year":"2000","author":"Rheinisch-Westf\u00e4lisches Institut f\u00fcr Wirtschaftsforschung.","key":"26_CR6","unstructured":"Rheinisch-Westf\u00e4lisches Institut f\u00fcr Wirtschaftsforschung. Arbeitsbericht 2000. Rheinisch-Westf\u00e4lisches Institut f\u00fcr Wirtschaftsforschung, Essen, Germany, 2000."},{"key":"26_CR7","unstructured":"U. Heilemann and H. J. M\u00fcnch. West German Business Cycles 1963-1994: A Multivariate Discriminant Analysis. CIRET-Conference in Singapore, CIRET-Studien 50, 1996."},{"key":"26_CR8","unstructured":"U. Heilemann and H. J. M\u00fcnch. Classification of German Business Cycles Using Monthly Data. SFB-475 Technical Reports 8\/2001. Universitaet Dortmund, 2001."},{"key":"26_CR9","unstructured":"Frank H\u00f6ppner. Learning temporal rules from state sequences. In Miroslav Kubat and Katharina Morik, editors, Workshop notes of the IJCAI-01 Workshop on Learning from Temporal and Spatial Data, pages 25\u201331, Menlo Park, CA, USA, 2001. IJCAI, AAAI Press. Held in conjunction with the International Joint Conference on Artificial Intelligence (IJCAI)."},{"key":"26_CR10","doi-asserted-by":"crossref","unstructured":"Eamonn Keogh, Selina Chu, David Hart, and Michael Pazzani. An online algorithm for segmenting time series. In Nick Cercone, T. Y. Lin, and Xindong Wu, editors, Proceedings of the 2001 IEEE International Conference on Data Mining, pages 289\u2013296, San Jose, California, 2001. IEEE Computer Society.","DOI":"10.1109\/ICDM.2001.989531"},{"key":"26_CR11","unstructured":"J\u00f6rg-Uwe Kietz and Stefan Wrobel. Controlling the complexity of learning in logic through syntactic and task-oriented models. Arbeitspapiere der GMD 503, GMD, mar 1991."},{"key":"26_CR12","doi-asserted-by":"crossref","unstructured":"Katharina Morik. The representation race\u2014preprocessing for handling time phenomena. In Ramon L\u00f3pez de M\u00e1ntaras and Enric Plaza, editors, Proceedings of the European Conference on Machine Learning 2000 (ECML 2000), volume 1810 of Lecture Notes in Artificial Intelligence, Berlin, Heidelberg, New York, 2000. Springer Verlag Berlin.","DOI":"10.1007\/3-540-45164-1_2"},{"key":"26_CR13","unstructured":"Ursula Sondhauss and Claus Weihs. Incorporating background knowledge for better prediction of cycle phases. Technical Report 24, Universit\u00e4t Dortmund, 2001."},{"key":"26_CR14","unstructured":"Winfried Theis and Claus Weihs. Clustering techniques for the detection of business cycles. SFB475 Technical Report 40, Universit\u00e4t Dortmund, 1999."},{"key":"26_CR15","unstructured":"Claus Weihs Ursula Sondhau\u03b2. Using labeled and unlabeled data to learn drifting concepts. In Miroslav Kubat and Katharina Morik, editors, Workshop notes of the IJCAI-01 Workshop on Learning from Temporal and Spatial Data, pages 38\u201344, Menlo Park, CA, USA, 2001. IJCAI, AAAI Press. Held in conjunction with the International Joint Conference on Artificial Intelligence (IJCAI)."},{"key":"26_CR16","unstructured":"Ian Witten and Eibe Frank. Data Mining \/\/ Practical Machine Learning Tools and Techniques with JAVA Implementations. Morgan Kaufmann, 2000."},{"key":"26_CR17","first-page":"35","volume":"40","author":"D. A. Zighed","year":"1999","unstructured":"D. A. Zighed, S. Rabaseda, R. Rakotomalala, and Feschet F. Discretization methods in supervised learning. In Encyclopedia of Computer Science and Technology, volume 40, pages 35\u201350. Marcel Dekker Inc., 1999.","journal-title":"Encyclopedia of Computer Science and Technology"}],"container-title":["Lecture Notes in Computer Science","Machine Learning: ECML 2002"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/3-540-36755-1_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,2,25]],"date-time":"2019-02-25T01:32:38Z","timestamp":1551058358000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/3-540-36755-1_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2002]]},"ISBN":["9783540440369","9783540367550"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/3-540-36755-1_26","relation":{},"ISSN":["0302-9743"],"issn-type":[{"type":"print","value":"0302-9743"}],"subject":[],"published":{"date-parts":[[2002]]}}}