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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. Dimensionality Reduction for Big Data

13. Smart Data

14. Introduction

15. Final Thoughts: From Big Data to Smart Data

16. Distance Metric Learning with Prototype Selection for Imbalanced Classification

17. Agglomerative Constrained Clustering Through Similarity and Distance Recalculation

19. Imbalanced Classification with Multiple Classes

20. Performance Measures

21. Algorithm-Level Approaches

22. Dimensionality Reduction for Imbalanced Learning

23. Ensemble Learning

24. Cost-Sensitive Learning

25. Introduction to KDD and Data Science

26. Data Level Preprocessing Methods

27. Foundations on Imbalanced Classification

28. Imbalanced Classification for Big Data

29. Data Intrinsic Characteristics

30. Non-classical Imbalanced Classification Problems

31. Learning from Imbalanced Data Streams

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

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

36. Discretization

37. Instance Selection

38. Feature Selection

39. Data Reduction

40. Dealing with Missing Values

41. Dealing with Noisy Data

42. Data Preparation Basic Models

45. Introduction

49. Managing Monotonicity in Classification by a Pruned AdaBoost

50. A Nearest Hyperrectangle Monotonic Learning Method

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