Data Mining and Machine Learning Applications. Группа авторов

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Название Data Mining and Machine Learning Applications
Автор произведения Группа авторов
Жанр Базы данных
Серия
Издательство Базы данных
Год выпуска 0
isbn 9781119792505



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      1 Chapter 1Table 1.1 Comparison in a data warehouse—OLTP.

      2 Chapter 2Table 2.1 Shows social media network applies to various data services.

      3 Chapter 3Table 3.1 Show the advantage and disadvantage of different types of RS.Table 3.2 Shows which technique, evaluation criteria are used in different RS.Table 3.3 Base of usage predication.Table 3.4 Comparative study of hybrid approach with traditional approach.

      4 Chapter 5Table 5.1 The transformation from word to lexeme.Table 5.2 Transformation to an extraordinary word list.Table 5.3 Vector portrayal of a report corpus.Table 5.4 The portrayal of the corpus utilized in this examination.Table 5.5 Groups with various Silhouette an incentive for every calculation.

      5 Chapter 6Table 6.1 Data mining developments qualified statement.Table 6.2 Shows application and usage of data mining.Table 6.3 Understudy related factors.Table 6.4 High potential variables.Table 6.5 Analysis of various classifiers.Table 6.6 Comparative analysis of various classifiers with their precision rates...

      6 Chapter 7Table 7.1 Number of tickets raised by 24 accounts/day and closed tickets/day: da...Table 7.2 Overfitness, testing error and accuracy of the random forest model.

      7 Chapter 8Table 8.1 Buyer counts.Table 8.2 Analysis of maximum likelihood estimates.

      8 Chapter 11Table 11.1 Shows cash flow statement source field in SAP System.Table 11.2 Shows key figures, dimensions, and characteristics of infocube.

      9 Chapter 13Table 13.1 Summarized detail of source and target datasets.

      10 Chapter 15Table 15.1 Different waveforms present in the brain.Table 15.2 CHB-MIT patient wise description.Table 15.3 Ictal (Seizure), Inter-ictal (Normal), and Pre-ictal (Partial Seizure...Table 15.4 Performance evaluation measures.Table 15.5 Performance metric when learning rate is set to 0.001.Table 15.6 Performance metric when learning rate is set to 0.01.Table 15.7 Performance metrics when learning rate is set to 0.1.Table 15.8 Comparative analysis of already proposed methodologies.

      11 Chapter 16Table 16.1 Labels for every forecast.Table 16.2 Predictions mean various percentiles limits.

      Guide

      1  Cover

      2  Table of Contents

      3  Title Page

      4  Copyright

      5  Begin Reading

      6  Index

      7  End User License Agreement

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