Data Visualizations

A brief description of Data Visualizations. On how and why are they created.

1. - @Vikram_Tiwari CS-B (0901010118) DATA VISUALIZATION
2. Data Visualization •Process by which raw data (basically numerical) are converted into meaningful data (mostly Graphs and Images) •Intended to analyze complex data •Providing new answers to old questions •Developing new knowledge and understanding through discovery •Data from: Satellite Photos, Sonar Measurements, Surveys, or Computer Simulations
3. Scientific Data •Any data or data-formats used in science or engineering can be referred as scientific data •In most of applications, hereby, we define scientific data as massive and digital data with a variety of data formats •Data can be floating-point data, integer data, image data, and clip data •Format are various. •Data dimensions (1-D, 2-D, 3-D or more)
4. What is Data Mining (DM)? •Set of activities used to find new, hidden, or unexpected patterns in data. •A data warehouse is main source where all data is stored. Example: databases •Research may be used for marketing or Customer Relationship Management.
5. Four Major Categories 1. Classification 2. Association 3. Sequence 4. Cluster Data mining methods may be classified by the function they perform or by their class of application.
6. • Mining processes intended to discover rules that define whether an item belongs to a particular class of data • Two Sub-processes: • Building a Model • Predicting Classifications • Techniques that employ association search all details from operational systems for patterns with a high probability of repetition • Example: Market Basket Analysis Classification Association
7. • Time series analysis methods relate events in time based on a series of preceding events • Through analysis, various hidden trends, often highly predictive of future events, can be discovered. • Example: Mail Industry • To create partitions so that all members of each set are similar according to some metric • Simply a set of objects grouped together by virtue of their similarity or proximity to each other • Example: Credit Card Transactions Sequence Cluster
8. Data Mining Techniques There are numerous techniques that are available to assist in mining the data. 1. Statistical Analysis 2. Neural Networks 3. Genetic Algorithms and Fuzzy Logic 4. Decision Trees
9. Applications of Data Mining •Two new categories of applications • Text Mining: Summarizes, navigates, and clusters documents contained in a database •Web Mining: Integrates data and text mining within a Web site; enhances the Web site with intelligent behavior, such as suggesting related links or recommending new products to the consumer
10. Market Basket Analysis •Market Basket Analysis is an algorithm that examines a long list of transactions in order to determine which items are most frequently purchased together. •It takes its name from the idea of a person in a supermarket throwing all of their items into a shopping cart (a "market basket").
11. Data Visualization •Technique/tool used to help researchers understand and/or interpret data. •It is a specific area in visualization, although it utilizes the same or similar techniques as used in other visualization. •The data visualization deal with rare data, which is difficult to understand. •Closed with data analysis methods and techniques.
12. Data Visualization Techniques •Plotting (Data Analytics) •Mapping (Graphing) •Color Image Interpretation (Image Processing) •Volume Rendering (Volume Visualization) •Graphics (Glut, OpenGL etc.) •Animation (Flash) •Virtual reality (CaveLib, OpenGL etc.) •Internet (WebGL, JQuery) •Database and data management
13. Basic Tec Plot guide •Creating an X-Y Plot •Creating a Contour Plot with Structured Data
14. Basic Tec Plot guide •Creating a Shaded Contour Plot •Creating an ISO-Surface Plot
15. Basic Tec Plot guide •Creating a Slice Plot •Creating a Stream trace Plot
16. Human Visual Perception • Human visual cortex dominates our perception • Accelerates the identification of hidden patterns in data • “A picture is worth a thousand words”