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Datamining data visualization
- 1. Data Mining Topic: DataVisualization Name: Shahmeer Roll No: 133 Semester: 6th Morning
- 2. Introduction ● Data visualizationaims to communicate data clearly and effectively through graphical representation. ● Data visualization is the graphical representation of information and data using visual elements like charts, graphs, and maps. It helps to see and understand trends, outliers, and patterns in data. ● Data visualization has been used extensively in many applications—for example, at work for reporting, managing business operations, and tracking progress of tasks.
- 3. Continued ● More popularly,we can take advantage of visualization techniques to discover data relationships that are otherwise not easily observable by looking at the raw data. ● Nowadays, people also use data visualization to create fun and interesting graphics. ● There are many basic concepts of Data visualization, ranging from different types to terminologies that are used in the subject of data mining. ● The limbo and concepts relating to the subject are necessary to understand.
- 4. Continued ● Some commonlystudied approaches include, pixel-oriented techniques, geometric projection techniques, icon-based techniques, and hierarchical and graph-based techniques. ● The visualization of complex data and relations is also deeply emphasized in the subject. ● All of these approaches will be cleared in this presentation.
- 5. Data Visualization Advantages Fasterand easier processing: Visual content is processed much faster and easier than text by the human brain. Data visualization helps to analyze data quickly and accurately. Simple data sharing: Data visualization makes sharing data simple and easy because it ensures that everyone is on the same page when they’re viewing the visualization. It also helps to tell a story with data and engage the audience. Better analysis: Data visualization helps to spot trends, patterns, outliers, and relationships in data that might otherwise be missed
- 6. Continued Quicker decisions: Datavisualization helps to make informed decisions based on data insights. It also helps to test hypotheses and scenarios and evaluate outcomes. Exploration and collaboration: Data visualization helps to explore data in an interactive and creative way. It also fosters collaboration and communication among different stakeholders and teams.
- 7. Data Visualization Disadvantages ●Biased or inaccurate information: Data visualization can be misleading or distorted if the data is not accurate, complete, or representative. It can also be influenced by the choice of colors, scales, shapes, and labels. ● Correlation vs causation: Data visualization can show correlations between variables, but not necessarily causation. It can also be affected by confounding factors or spurious relationships that are not relevant or meaningful.
- 8. Continued ● Complexity andclutter: Data visualization can be complex and cluttered if it tries to show too much information or too many dimensions. It can also be confusing or overwhelming if it is not clear, consistent, or intuitive.
- 9. Data Visualization Examples- Bar Chart ● A bar chart uses horizontal or vertical bars to show the values of different categories or groups. It is useful for comparing data across categories or showing changes over time. For example, you can use a bar chart to show the sales of different products in each quarter.
- 10. Line Chart ● Aline chart uses points connected by lines to show the values of a variable over time or along a scale. It is useful for showing trends, patterns, or fluctuations in data. For example, you can use a line chart to show the stock price of a company over a year.
- 11. Pie Chart ● Apie chart uses a circular shape divided into slices to show the proportions of different categories or groups. It is useful for showing the relative sizes of parts of a whole. For example, you can use a pie chart to show the market share of different brands in a sector.
- 12. Pixel-Oriented Visualization Techniques ●Pixel-oriented data visualization technique is a way of showing a lot of information on a screen by using small dots of different colors. ● Each dot represents one piece of information and each color means something different. ● For example, you can use this technique to show how many people live in different countries by using different colors for different population ranges.
- 13. Continued ● This techniquecan help users see patterns and differences in the information more easily. Since each pixel represents a separate set of data, it makes it easier for a lot of data to be shown on the screen at any given moment. ● This technique uses small dots, called pixels, to show the information. Each pixel has a color that means something.
- 14. Continued ● For example,pixels can be put in a line or a circle or a spiral. By using this technique, a lot of information can be seen at once and compared easily; it is possible because of the sheer amount of pixels that can be presented on a screen at once. ● Imagine you have a lot of information about different things, like cars, animals, or countries. You want to see this information on a screen, but there is too much to fit in a normal way. So you use a technique called pixel-oriented data visualization
- 15. Continued ● Data canbe put in a spiral or a circle. Moreover, different windows can also be used to represent data. ● For example, you can use one window to show the speed of the cars, another window to show the price of the cars, and another window to show the size of the cars. ● For another example, you can compare the population, the area, and the GDP of different countries by using three windows with different colors for each attribute.
- 16. Diagram example ● Wecan sort all customers in income-ascending order, and use this order to lay out the customer data in the four visualization windows, as shown in Figure below. The pixel colors are chosen so that the smaller the value, the lighter the shading.
- 17. How it isused in data mining? One example of pixel-oriented visualization is the heat map, which is commonly used in data mining and machine learning. Heat maps use color to represent the values of a matrix or table, with darker colors indicating higher values and lighter colors indicating lower values. Heat maps are commonly used to visualize correlation matrices, gene expression data, and other types of data.