Dataset

• Instance == object, record, sample, entity, observation
• Attribute == characteristic, field, feature, dimension

Data Categorization

• Numerical : Made of numbers
  1. Continuous : Infinite options (Age, weight, blood pressure)
  2. Discrete : Finite options (Shoe size, number of children)
• Categorical : Made of words
  1. Ordinal : Data has a hierachy (Satisfaction rating, mood)
  2. Nominal : Data has no hierachy (Eye color, blood type)

Data Types

1. Record Data

   • The most widely used data type
   • Consist of a collection of records
   • Each record is compsed of a fixed number of attributes
     

2. Transaction data

   • Consist of a buyer and a list of purchased items dataset

3. Graph-based data

Data Preprocessing

• An important step in the data mining process
• Contain the cleaning, transforming, and integrating of data in order to analysis
• (goal) Improve the quality of data and make the data suitable for the specific task
  • Garbage in, garbage out... (Importance of data preprocessing)

1. Data Cleaning : Remove noise and correct inconsistencies in data

2. Data integration : Merge data from multiple sources into a coherent data store such as a data warehouse

3. Data transformation / Discretization : Data are scaled to fall within a smaller range like 0 ~ 1 (Normalization)
   

4. Data reduction : Reduce data size by aggregating, eliminating redundant features, or clustering

(Jupyter Notebook) 헷갈린 부분

1. Data Understanding Jupyter Notebook
2. Data Understanding Jupyter Notebook