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A double clutch transmission in connection with an electric unit, as well as a method for operating the transmission.

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A double clutch transmission in connection with an electric unit, as well as a method for operating the transmission. ion Algorithm (K-Mean). Academic Press Chap. 11 pp. 515-523 (1990). K. Fukunaga. "Statistical Pattern Recognition". Maximum Likelihood. Academic Press Chap. 11 pp. 527-532 (1990).

A double clutch transmission in connection with an electric unit, as well as a method for operating the transmission. ion Algorithm (K-Mean). Academic Press Chap. 11 pp. 515-523 (1990). K. Fukunaga. "Statistical Pattern Recognition". Maximum Likelihood. Academic Press Chap. 11 pp. 527-532 (1990). S. Agarwal et al. "On the Computation of Multidimensional Aggregates." Proc. 22nd Int. VLDB Conf., Mumbai (Bombay), (1996). J. Banfield, A Raftery, "Model-based Gausian and Non-Gausian Clustering." Biometrics, vol. 49:803-821, (1993). D. Barbara, M. Sullivan, "A Space Efficient Way to Support Approximate Multidimensional Databases". George Mason University Technical Report ISSE-TR-98-03, (1998). K.S. Beyer, R. Ramakrishnan, "Bottom-Up Computation of Sparse Iceberge Cubes", Proc. ACM SIGMOD Conf., Philadelphia, (1999) (to appear). P.S. Bradley et al., "Clustering via Concave Minimization", Advances in Neural Information Processing Systems 9, MIT Press, (1997). P.S. Bradley et al., "Scaling Clustering Algorithms to Large Databases", Proc. 4th Intl. Conf. On Knowledge Discovery and Data Mining (KDD98), AAAI Press, (1998). P.S. Bradley et al., "Scaling EM Clustering to Large Databases", Microsoft Research Technical Report MSR-TR-98-35, (1998). P.M. Deshpande et al., "Caching Multidimensional Queries Using Chunks", Proc. ACM SIGMOD Conf., Seattle, (1998). D. Fisher, "Knowledge Acquisition Via Incremental Conceptual Clustering", Machine Learning, 2:139-172, (1987). J. Gray et al., "Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab and Sub-Totals", Data Mining and Knowledge Discovery, 1(1), pp 29-53, (1997). H. Gupta et al., "Index Selection for OLAP", Proc. Intl. Conf. On Data Engineering, Birmingham, UK, (Apr. 1997). V. Harinarayan et al., "Implementing Data Cubes Efficiently", Proc. ACM SIGMOD Conf. Montreal, (1996). R. Kohavi, "Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision Tree Hybrid". Proc. of the 2nd International Conf. On Knowledge Discovery and Data Mining. AAA1 Press, (1996). Y. Kotidis, N. Rousopoulos, "An Alternative Storage Organization for ROLAP Aggregate Views Based on Cubetrees", Proc., ACM SIGMOD Conf., Seattle, (1998). I. Mumick et al., "Maintenance of Data Cubes and Summary Tables in a Warehouse", Proc. ACM SIGMOD Conf. Tucson, (1997). V. Poosala, Y. E. Ionnidis, "Selectivity Estimation without the Attribute Value Independence Assumption", Proc. 23rd VLDB Conf., Athens, Greece, (1997). S. Z. Selim, M. A. Ismail, "K-Means Type Algorithms: A Generalized Convergence Theorem and Characterization of Local Optimality", IEEE Trans. Pattern Analysis and Machine Intelligence, vol. PAMI-6, No. 1, (1984). A. Shulka et al., "Storage Estimation for Multidimensional Aggregates in the Presence of Hierarchies", Proc. 22nd Int. VLDB Conf. Mumbai, Bombay, (1996). J. S. Vitter et al., "Data Cube Approximation and Histograms via Wavelets", Proc. 7th Intl. Conf. Information and Knowledge Management (CIKM 98), Washington, D.C., (Nov. 1998). T. Zhang, "BIRCH: An Efficient Data Clustering Method for Very Large Databases", Proc. ACM SIGMOD Conf. Montreal, (1996). Y. Zhao et al., "An Array-Based Algorithm for Simultaneous Multidimensional Aggregates", Proc. ACM SIGMOD Conf., Tucson, (1997). Y. Zhao et al., "Simultaneous Optimization and Evaluation of Multiple Dimensional Queries", Proc. ACM SIGMOD Conf., Seattle, (1998). is then classified based on the tiling information. A sorted version of the classified data, ordered by cluster number and then by the tile number within the cluster is generated. This data is then evaluated to test the sufficiency of the model created during the clustering.