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1. Lose the Memory, Lose the History, Lose the City

2. Adapting K-Means Algorithm for Pair-Wise Constrained Clustering of Imbalanced Data Streams

3. Monotonic Constrained Clustering: A First Approach

4. A Preliminary Approach for using Metric Learning in Monotonic Classification

5. Modeling Human Decision-Making Delays and Their Impacts on Supply Chain System Performance: A Case Study

6. Data Reduction for Big Data

7. Imperfect Big Data

8. Big Data: Technologies and Tools

9. Big Data Software

10. Big Data Discretization

11. Imbalanced Data Preprocessing for Big Data

12. Smart Data

13. Introduction

14. Final Thoughts: From Big Data to Smart Data

15. Distance Metric Learning with Prototype Selection for Imbalanced Classification

16. Agglomerative Constrained Clustering Through Similarity and Distance Recalculation

18. Imbalanced Classification with Multiple Classes

19. Performance Measures

20. Algorithm-Level Approaches

21. Dimensionality Reduction for Imbalanced Learning

22. Ensemble Learning

23. Cost-Sensitive Learning

24. Introduction to KDD and Data Science

25. Data Level Preprocessing Methods

26. Foundations on Imbalanced Classification

27. Imbalanced Classification for Big Data

28. Data Intrinsic Characteristics

29. Non-classical Imbalanced Classification Problems

30. Learning from Imbalanced Data Streams

31. On the Use of Random Discretization and Dimensionality Reduction in Ensembles for Big Data

34. rNPBST: An R Package Covering Non-parametric and Bayesian Statistical Tests

35. Discretization

36. Instance Selection

37. Feature Selection

38. Data Reduction

39. Dealing with Missing Values

40. Dealing with Noisy Data

41. Data Preparation Basic Models

44. Introduction

48. Managing Monotonicity in Classification by a Pruned AdaBoost

49. A Nearest Hyperrectangle Monotonic Learning Method

50. Managing Monotonicity in Classification by a Pruned Random Forest

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