DATA MINING - CHARACTERISTICS and APPLICATION
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The document discusses data mining and machine learning, defining data mining as the extraction of useful information from vast datasets and machine learning as the development of algorithms that enhance through data experience. It highlights key characteristics of structured data such as dimensionality, sparsity, resolution, and distribution, along with common data quality issues like noise, outliers, and duplicates. Additionally, it emphasizes the importance of data visualization for understanding trends and informs various application areas including market analysis, healthcare, and manufacturing.
DATA MINING - CHARACTERISTICS and APPLICATION
- 1. D A T A M I N I N G & M A C H I N E L E A R N I N G DA F F O D I L I N T E R N AT I O N A L U N I V E R S I T Y Md.Anisur Rahman
- 2. Contents 1)Data Mining & Machine Learning 2)Data 3)Exploring data -Visualization 4)Data Mining and ML Techniques 5)Applications 6)Summary
- 3. DATA MINING Data mining is considered the process of extracting useful information from a vast amount of data. It’s used to discover new, accurate, and useful patterns in the data, looking for meaning and relevant information. MACHINE LEARNING Machine learning is the process of discovering algorithms that have improved courtesy of experience derived from data. It’s the design, study, and development of algorithms that permit machines to learn without human intervention. Both data mining and machine learning fall under the aegis of Data Science, which makes sense since they both use data. Both processes are used for solving complex problems, so consequently, many people (erroneously) use the two terms interchangeably.
- 4. DATA Collection of data objects and their attributes. A collection of attributes describe an object. -record, point, case, sample, entity, or instance property or characteristic of an object -eye color of a person, temperature, variable, field, characteristic, or feature TYPES OF ATTRIBUTES Nominal Order Interval Ratio zip codes, employee ID numbers, eye color, sex: {male, female} hardness of minerals, {good, better, best}, grades, street numbers calendar dates, temperature in Celsius or Fahrenheit temperature in Kelvin, monetary quantities, counts, age, mass, length, electrical current
- 5. IMPORTANT CHARACTERISTICS OF STRUCTURED DATA 1)Dimensionality Dimensionality is basically the number of columns in a dataset which also can be called the attributes of data. If we add too many dimensions, this can potentially make the data incredibly difficult to analyze because it becomes so different, and difficult to group together, the data in a meaningful way. 2)Sparsity Data sparsity is term used for how much data we have for a particular dimension/entity of the model. Data is considered sparse when certain expected values in a dataset are missing, which is a common phenomenon in general large scaled data analysis. 3)Resolution Data resolution means a number of units or digits to which a measured or calculated value is expressed and used. Patterns depend on the scale; think about weather patterns, rainfall over a time period. 4)Distribution Data distributions are used often in statistics.They are graphical methods of organizing and displaying useful information.There are several types of data distributions.We are familiar with the symmetrical and skewed distribution
- 6. Record • Data Matrix • Document Data • Transaction Data Graph • World Wide Web • Molecular Structures Order • Spatial Data • Temporal Data • Genetic Sequence etc.
- 7. DATA QUALITY Noise and Outliers • Noise refers to modification of original values • Outliers are data objects with characteristics that are considerably different than most of the other data objects in the data set. MissingValues • Information is not collected • Attributes may not be applicable to all cases • We can handle missing values by eliminating missing values or filling them with statistical approach Duplicate Data • Data set may include data objects that are duplicates, or almost duplicates of one another. • Major issue when merging data from heterogeneous sources. • Data cleaning can solve the problem for duplication of data.
- 9. DATA VISUALIZATION Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data. Data visualization tools and technologies are essential to analyze massive amounts of information and make data-driven decisions.
- 10. TECHNIQUES
- 11. Market Based Analysis Education Manufacturing Engineering Research Analysis Fraud Detection APPLICATIONS
- 12. Market Based Analysis Digital Midea & Entertainment Manufacturing & Automobile E- Commerce & CRM Healthcare APPLICATIONS
- 14. THANK YOU