Types of dataset

Types of dataset

Types of Dataset

1) Important Characteristics of Data

① Dimensionality (# of attributes)

• High dimensional data brings many challenges to analyze

② Sparsity

• Only presence counts
• Proportion of missing, NaN, set to 0 data

③ Resolution

• Patterns depend on the scale
• Quality, detail of the data

④ Size

• Type of analysis may depend on the size of the data
• Overfitting, Underfitting problems (discussed later)

• 2) Types

① Record Data

①-① Data Matrix

Data with collection of records, which consists of a fixed set of attributes

• Could be named as m x n matrix: m rows x n columns, one for each attribute

• When there are data objects with a fixed set of numeric attributes, able to be thought of as points in a multi-dimensional space, each dimension represents a distinct attribute

①-② Document Data

• Each document becomes a ‘term’ vector
• Each term is a component (attribute) of the vector
• The value of each component is the number of times the corresponding term occurs in the document

①-③ Transaction Data

• Each transaction involves a set of items
• Several items included in one attribute
• Can represent transaction data as record data

② Graph Data

• Worldwide Web

• Molecular Structures

③ Ordered Data

• Spatial Data

• Temporal Data

• Sequential Data

Sequences of transactions

• Genetic Sequence Data

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