Data Mining Vs Data Visualization – Which One Is Better

Introduction to Data Mining and Data Visualization

Data Mining and Data Visualization come under the field of Data Science which is an interdisciplinary field of computer science having statistics, computing, mathematics and several technical processes including different methodologies.

Data Mining is a part of Data Science where there will be a process of going through large data sets and identifying the data sets and data types to extract different data patterns out of the existing data sets.

Data Visualization is the process of extracting and visualizing the data in a very clear and understandable way without any form of reading or writing by displaying the results in the form of pie charts, bar graphs, statistical representation and through graphical forms as well.

In Data Mining, there are different processes involve to carry out the data mining process such as data extraction, data management, data transformations, data pre-processing etc.

In Data Visualization, the primary goal is to convey the information efficiently and clearly without any deviations or complexities in the form of statistical graphs, information graphs, and plots.let us discuss both Data Mining and Data Visualization in detail.

Head to Head to Comparison between Data Mining vs Data Visualization (Infographics)

Below is the Top 7 Comparison between Data Mining vs Data Visualization

Key Differences between Data Mining vs Data Visualization

1.Data Mining is the process of sorting out some large data sets and extracting some data out of them and extracting patterns out of the extracted data whereas Data Visualization is the process of visualizing or displaying the data extracted in the form of different graphical or visual formats such as statistical representations, pie charts, bar graphs, graphical images etc.
2.Data Mining processes include sequences analysis, classifications, path analysis, clustering and forecasting whereas In Data Visualization contains processing, analyzing, communicating the data etc.
3.In Data Mining, the data will be displayed automatically in search process which will be displayed by the system analysis itself whereas Data Visualization, gives a clear view of the data and will be easy for the human brain to remember and memorize large chunks of data at a single glance.
4.In Data Mining, there are four stages which are Data Sources, Data gathering or data exploring, data modeling and deploying the data models whereas In Data Visualization has seven stages which are acquiring process, parsing, filtering, mining, representing, refining and interacting.
5.Data Mining is a group of different activities to extract different patterns out of the large data sets in which data sets will be retrieved from different data sources whereas Data Visualization is a process of converting numerical data into graphical images like meaningful 3D pictures which will be used to analyze complex data easily.
6.The applications of Data Mining include Customer Relationship Management which is a software application that provides advantages to data mining whereas the applications of Data Visualization include sonar measurements, satellite photos, computer simulations and surveys etc.
7.The different techniques available in Data Mining are Classification, Cluster, Sequence, Association etc. Data Visualization has originated from statistics and sciences which give clear visualization at a glance which means a picture gives 100 words at its sight.
8.In Data Mining, classification is the process of identifying the rule of the data whether it belongs to a particular class of data or not and its’ sub-processes include building a data model and predicting the classifications whereas In Data Visualization the main application include geographical information systems where the important geographical information can be represented as visual images that represent complex information as simple as possible.
9.Data mining technologies also include neural networks, statistical analysis, decision trees, genetic algorithms, fuzzy logic, text mining, web mining etc., whereas the Data Visualization has different applications such as retail, government, medicine and healthcare, transportation, telecommunication, insurance, capital markets and asset management.
10.The limitations in Data Mining are such as even it is being new technology but it is still underdeveloped one because of many companies using legacy systems and also the existing systems are not data warehouse friendly Data Visualization has significant disadvantages in its tools are such as it shows different visuals rather than explaining, no guidelines, different users with multiple insights and also provides poor security.
11.Data Mining is an analytical process that identifies different patterns from the data sets which can help in dealing with the flood of information and Data Visualization provides a lot of visualization techniques which have been developed for the past decades those support the exploration of large data sets.
12.The benefit of Data Mining is that the relationship will be unhidden among different data sets and variables whereas Data Visualization defines as it is the visual object by representing the data in the form of graphs and charts.

Data Mining vs Data Visualization Comparison Table

Conclusion – Data Mining vs Data Visualization

Data mining is an area of Data Science where the large data sets will be thoroughly processed to provide with suitable results in the search by identifying different patterns.

Data Visualization is the process of displaying visual information out of the existing complex data to draw a particular conclusion at a glance without the need of studying any theoretical results. The applications include satellite data information, research results information, scientifically studied data etc.

The applications of the Data mining are web search engines, retail, financial and banking industries, government organizations etc. Both the data mining and data visualization

have great advantages in the area of data science applications in computer science field.

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