With the increasing possibility of datasets with a vast amount of information coded with many peculiar features confirms the research on latest methods of knowledge extraction.
The great imposition is the translation of the raw data into the useful information which is used to improve the decision-making process by detecting the significant profiles and figuring out the relationships among features.
As “Seeing is Believing” assures the importance of visualization in the real-world applications of machine learning which provides some apprises to the current state and the near future of the visualization methods within the framework of machine learning.
As data exploration is one of the vital building blocks or constituting pages of the most standard in data mining and knowledge discovery in database methodologies which is part of the more generic phase of data understanding.
It is referred to as the use of techniques, from data querying and basic statistics to advanced visualization for discovering the main characteristics of usually complex multivariate data sets by help in bringing the important aspects of the data into spotlight for study in consequent phases of the analysis.
Data visualization plays a decisive role in identifying interesting patterns in exploratory data analysis. Though it’s crucial, the usage of it is however, made difﬁcult by the bulk number of possible data projections showing different attribute subsets that must be evaluated by the data analyst.
Because real-life data sets contain several attributes, ﬁnding interesting projections will be a difﬁcult and time-consuming task, since the number of possible projections increases exponentially with the number of simultaneously visualized attributes.
One of the main roles of data mining algorithms in visualization methods consists in providing some form of scalability. As human vision is strongly limited to 2D/3D displays, with the additional ability to decode shapes and colors efficiently.
Thus, no more than these variables can be displayed at a time, wherein the number of data points remains finite in order to avoid overlapping. Then, feature selection dimensionality reduction and clustering are methods of choice as preprocessing solutions for medium to large scale data visualization.
Visualization is one of the keystones of knowledge extraction from huge datasets. Hierarchical approaches appear as a legitimate solution after all the global methods generating a single picture of the data may provide which may be either too complicated or condensed visualizations as they lack the detail vital for data understanding and knowledge extraction.
These methods produce visualization at different levels of the hierarchy by obtaining both the main course relationships and particular information. The advanced visualization techniques make use of clustered graphs based on hierarchical maximal modularity clustering where the general layout of the graph is given by the higher level of the hierarchy in which it is robustly simplified, and then details are added in a top-down way by declining along with the hierarchy.
Visualization in Manifold Learning:
The theme of visualization in manifold learning more specifically, the self-organized maps plays a far-reaching role in some of the contributions.
The most explicit use of maps can be found in which resorts to organized mapping visualization to infer the relationships among the different parameters that need to be tuned in Machine Learning algorithms, by helping to find the optimal combination of parameters that escalates the performance of the algorithm.
Although the approach presented particularly applied to Reinforcement Learning, the strategy is completely general which is applicable to any other kind of algorithms.
Hierarchical visualizations and manifold learning compose of two main machine learning approaches to yield visualizations that can extract knowledge from data sets.
The data available for the processing gets ever bigger, more convoluted, structured and heterogeneous.
Therefore, latest methods are applied for extracting the knowledge of the data from the datasets in the straightforward way which has an automatic effect in real practice since it turns out appealing results which can be easily accepted by the application experts who are unaware of machine learning.