Big Data Mining: An overview on Machines Learning Algorithms

General Information:

Cover Page:

Outline:

1 General Introduction.
1.1 Introduction
1.2 Positioning of the problem.
1.3 Objective
1.4 Organization of the master thesis
Chapter I
Generalities & Concepts of Big Data
I.1 Introduction
I.2 definitions
1.3 Big Data Characteristics
1.4 Big Data Structuring.
1.4.1 Structured data
I.4.1.1 Machine-generated structured data includes:
I.4.1.2 Human-generated structured data includes:.
I.4.2 Semi-structured data.
I.4.3 Quasi-structured data
I.4.4 Unstructured data.
1.4.4.1 The unstructured data generated by the machine includes:
1.4.4.2 The unstructured data generated by the Human includes:.
1.5 Conclusion
Chapter II
Construction and Processing of Big Data Mining
II.1 Introduction
II.2 Data collection
II.2.1 Methods of data collection
II.2.1.1 Observation
II.2.1.1.1 Direct observation.
II.2.1.1.2 Indirect observation
II.2.1.2 Questionnaires
II.2.1.3 Interviews
II.2.1.4 Focus groups
II.2.1.4.1 Focus group applications
II.2.1.5 Documents
II.2.1.6 Concept map
II.3 Data preparation.
II.3.1 Data cleaning.
II.3.2 Data transformation
II.3.3 (Big) Data mining and analysis.
II.4 Data visualization
II.4.1 Data visualization in the past.
II.4.2 Visualization of (big) data today
II.4.3 Traditional concepts of data visualization
II.4.4 Interactive data visualization .
II.4.5 Very important advice in visualization methodology
II.5 Conclusion
III.1 introduction
III.2 Data Analytics.
Chapter III
Big Data Mining: Analytics and Technologies
III.2.1 Types of (Big) data analytics.
III.2.1.1 descriptive analytics.
III.2.1.2 diagnostic analytics
III.2.1.3 predictive analytics.
III.2.1.3.1 Predictive Data Analytics Project Lifecycle.
III.2.1.4 perspective analytics.
III.3 Hadoop and its ecosystem
III.3.1 Hadoop
III.3.2 Hadoop ecosystem
III.3.2.1 MapReduce programming model
III.3.2.2 Hadoop Distributed File System (HDFS).
III.3.2.2.1 HDFS architecture
III.3.2.3 Cassandra
III.3.2.4 HBase
III.3.2.5 Zookeeper.
III.3.2.6 Pig
III.3.2.7 Apache Hive
III.3.2.8 Flume.
III.3.2.9 Storm.
III.3.2.10 apache Spark
III.3.2.11 Kafka
III.4 Machine learning.
III.4.1 Objectives and uses of machine learning
III.4.2 types of machine learning.
III.4.2.1 Supervised learning
III.4.2 Unsupervised learning.
III.4.3 Reinforcement learning.
III.5 Conclusion
IV.1 Introduction
IV.2 Used tools versions
Chapter IV
Realization and Implementation
IV.3 Datasets
Why did we choose the five classifiers in our study?
IV.4 Experimentation
IV.4.1 Multiclass experimentation
IV.4 Evaluation Metrics
IV.5 Performance metrics result comparison
IV.5.1 Multiclass classification:
IV.5.2 binary classification:.
IV.6 Conclusion
General conclusion
References

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