# On the Role of Clustering and Visualization Techniques in Gene Microarray Data
## 1 Introduction  
Since the dawn of the new millennium, all science fields have experienced an impressive increase in available data, not only in terms of quantity but also in terms of quality and sharing, thus calling for new theories, techniques and tools to enable scientists and information stakeholders to properly exploit the oceans of distributed and diverse data in knowledge extraction.  
There are two primary elements to this shift: on the one side, in biology, astrophysics, social sciences, and in several other fields, traditional interactive information analysis and information visualization techniques have proven to be far insufficient to deal with data sets defined by enormous quantities and complexity (very high dimensionality, i.e., number of features). Second, the concurrent assessment of hundreds of features may reveal earlier unidentified patterns leading to a greater comprehension of the fundamental process dynamics and trends. Therefore, the field of Knowledge Discovery in Databases or KDD [1] is becoming of crucial significance not only in its traditional arena but also as an additional instrument for nearly all study areas.  
Specific information mining techniques for model identification and analysis [2] are at the heart of the method. In biology, researchers have been effective in cataloging genes through DNA sequencing initiatives and can now produce large amounts of information on gene expression using microarrays.  
Microarrays of gene expression, the growth of which began in the second quarter of the 1990s, have a strong effect on molecular biology. Indeed, while the capacity to determine a single gene expression is not new, the capacity to assess the expression of all DNA in an individual at once is the latest development and leads to fresh techniques of diagnosis and therapy for several kinds of illnesses. It is also becoming progressively apparent, however, that merely producing the data is not sufficient and extracting the appropriate information is far from trivial.  
Over the past two decades, a great deal of jobs have concentrated on developing machine learning approaches suitable for dna information assessment [3; 4; 5; 6].  
Clustering is one of the main instruments for analyzing genetic data [7]. The nature of the problem makes scientists consider clustering as an instrument not only to find coherent and consistent groupings in data, but also to relate to one another the elements of structure, functions and development.  
Many findings in experimental biology first occur as an image of an organism, cells, or microarray scans. As the amount of these outcomes accelerates, it becomes essential to automatically extract features and significance from those images.  
Naive \(2D\) or \(3D\) visualizations alone at the other end of the information pipeline are insufficient to explore genetical data. Biologists need visual instruments that promote the exploration of high-dimensional information based on many parameters. Data visualization is fundamental for extracting helpful information from big amounts of raw data. The human eye and brain together constitute an amazing model recognition tool, but for them to function, the data must be presented in a low-dimensional space, commonly two or three dimensions. Even a very simple association may appear very vague when the information is displayed in tabular type, but visual testing often makes it very easy to see. Visualization in bioinformatics needs a lot of research efforts for developing tools that enable to tackle the challenges of the present and future genomics and proteomics. These arguments motivate the content of the paper which provides a review of the recent developments in two specific data mining research directions, namely, clustering and visualization of data, in the perspective of genetic applications.