Page 170 - Building Big Data Applications

Chapter 9 Governance 169

not possible. At this stage is where we bring in machine learning techniques to process
data such as images, videos, graphical information, sensor data, and any other type of
data where patterns are easily discernible.
Machine learning can be defined as a knowledge discovery and enrichment process
where the machine represented by algorithms mimic human or animal learning techniques
and behaviors from a thinking and response perspective. The biggest advantage of incor-
porating machine learning techniques is the automation aspect of enriching the knowledge
base with self-learning techniques with minimal human intervention in the process.
Machine learning is based on a set of algorithms that can process a wide variety of
data that normally is difficult to process by hand. These algorithms include the
following:
Decision tree learning
Neural networks
Naive Bayes
Clustering algorithms
Genetic algorithms
Learning algorithms
Explanation-based learning
Instance-based learning
Reinforcement-based learning
Support vector machines
Associative Rules
Recommender algorithms
The implementation of the algorithms is shown in Fig. 9.6. The overall steps in
implementing any machine learning process are as follows:
1. Gather data from inputs
2. Process data through the knowledge-based learning algorithms, which observes the
data patterns and flags them for process. The knowledge learning uses data from

FIGURE 9.6 - Machine learning process.