lecture 1.docx

Here are the main points from the \"Introduction to Data Mining\" presentation, with brief explanations: 1. **Why Data Mining?** - The rapid growth of data is driven by the computerization of society and advancements in data collection and storage. Major data sources include...

Here are the main points from the \"Introduction to Data Mining\" presentation, with brief explanations: 1. **Why Data Mining?** - The rapid growth of data is driven by the computerization of society and advancements in data collection and storage. Major data sources include business, science, and social activities (e.g., web transactions, scientific simulations, social media). 2. **What is Data Mining?** - Data mining, also known as Knowledge Discovery in Data (KDD), is the process of discovering patterns from large datasets. It can involve various data sources like databases, data warehouses, and streamed data. 3. **Data Mining Process** - The process includes several steps: 1. **Data Cleaning**: Removing noise and inconsistencies. 2. **Data Integration**: Combining data from multiple sources. 3. **Data Selection**: Selecting relevant data for analysis. 4. **Data Transformation**: Preparing data for mining through summary or aggregation. 5. **Data Mining**: Extracting patterns using intelligent methods. 6. **Pattern Evaluation**: Identifying interesting patterns. 7. **Knowledge Presentation**: Presenting results using visualization techniques. 4. **Technologies Used in Data Mining** - Data mining requires algorithms from multiple disciplines due to the complexity and scale of data. These include handling high-dimensional, temporal, spatial, multimedia, and web data. 5. **Data Objects and Attributes** - Data sets consist of objects (e.g., customers, sales, patients), which are described by attributes. Attributes can be **nominal** (categories), **binary** (two states), **ordinal** (ranked), or **numeric** (quantitative values). 6. **Attribute Types** - Attributes are categorized into different types: 1. **Nominal**: Names or symbols with no meaningful order. 2. **Binary**: Two possible states (e.g., true/false). 3. **Ordinal**: Ranked values (e.g., size: small, medium, large). 4. **Numeric**: Measurable quantities, can be discrete or continuous. 1. **Data Visualization** - Visualization helps users gain insight into large datasets by mapping data to graphical representations. It helps identify patterns, trends, and irregularities, providing visual proof of the data\'s structure​(\_lecture01.pptx).