Data Mining - Introduction
- Attribute Types
- Basic Statistical Descriptions of Data
- Knowledge Discovery from Data (KDD)
- Data Mining architecture
- Types of Data
- Data Mining Functionalities
- Interestingness Patterns
- Classification of Data Mining systems
- Data mining Task primitives
- Integration of Data mining system with a Data warehouse
- Major issues in Data Mining
- Data Preprocessing
- Association Rule Mining
- Mining Frequent Patterns
- Associations and correlations
- Mining Methods
- Apriori algorithm
- FP-Growth algorithm
- Mining Various kinds of Association Rules
- Correlation Analysis
- Constraint based Association mining
- Graph Pattern Mining
- SPM(Sequential Pattern Mining)
- Classification
- Basic concepts
- Decision tree induction
- Bayesian classification
- Rule–based classification
- Lazy learner(K-NN)
Attribute Types
Attribute values are numbers or symbols assigned to an attribute. The type of the attribute can be determined based on the assigned value.
The set of possible values - nominal, binary, ordinal, or numeric - the attribute can have.
Nominal Attributes
- The values of a nominal attribute are symbols or names of things. Each value represents some kind of category, code, or state.
- Nominal attributes are also referred to as Qualitative and Categorical attributes.
- The values of nominal attributes do not have any meaningful order.
| Attributes | Possible Values |
| hair_color | black, brown, red, green, and so on. |
| marital_status | single, married, divorced, and widowed. |
| occupation | teacher, doctor, farmer, student and so on. |
The nominal attribute values do not have any meaningful order about them and they are not quantitative. So
- It makes no sense to find the mean (average) value or median (middle) value for such an attribute.
- However, we can find the attribute’s most commonly occurring value (mode)
Binary Attributes
A binary attribute is a special nominal attribute with only two states: 0 or 1. Where 0 typically means that the attribute is absent, and 1 means that it is present.
Symmetric Binary Attribute
A binary attribute is symmetric if both of its states are equally valuable and carry the same weight.
Example: the attribute gender having the states male and female.
Asymmetric Binary Attribute
A binary attribute is asymmetric if the outcomes of the states are not equally important.
Example: Test results for COVID patient: Positive (1) and Negative (0).
By convention, we code the most important outcome, which is usually the rarest one, by 1 (e.g., COVID positive) and the other by 0 (e.g., COVID negative).
Ordinal Attributes:
An ordinal attribute is an attribute with possible values that have a meaningful order or ranking among them, but the magnitude between successive values is not known.
- Ordinal attributes are also referred to as Qualitative and Categorical attributes.
Example: An ordinal attribute drink_size corresponds to the size of drinks available at a fast-food restaurant.
- This attribute has three possible values: small, medium, and large.
- The values have a meaningful sequence (which corresponds to increasing drink size);
- However, we cannot tell from the values how much bigger, say, a medium is than a large.
Ordinal attributes are useful in surveys, In one survey, participants were asked to rate how satisfied they were as customers.
Customer satisfaction had the following ordinal categories:
0: very dissatisfied
1: somewhat dissatisfied
2: neutral
3: satisfied
4: very satisfied.
The central tendency of an ordinal attribute can be represented by its mode and its median (middle value in an ordered sequence), but the mean cannot be defined.
Interval-Scaled Attributes
Interval-scaled attributes are measured on a scale of equal-size units. The values of interval-scaled attributes have order and can be positive, 0, or negative.We can compare and quantify the difference between values of interval attributes.
Examples:- A temperature attribute is an interval attribute.
- We can quantify the difference between values. For example, a temperature of 20oC is five degrees higher than a temperature of 15oC.
- Calendar dates is another example for an interval attribute.
- Temperatures in Celsius do not have a true zero point, that is, 0oC does not indicate “no temperature.”
- Calendar dates do not have a true zero point, that is, the year 0 not the beginning of the time.
Although we can compute the difference between temperature values, we cannot talk of one temperature value as being a multiple of another.
Without a true zero, we cannot say, for instance, that 10oC is twice as warm as 5oC. That is, we cannot speak of the values in terms of ratios.
The central tendency of an interval attribute can be represented by its mode, its median (middle value in an ordered sequence), and its mean Data.
Ratio Attribute
A ratio attribute is a numeric attribute with an inherent zero point.
Examples:- number_of_words in a documents object.
- count attribute such as years of experience for employee object.
- Attributes to measure weight, height, latitude, and longitude coordinates.
- With an amount attribute we can say “you are 100 times richer with $100 than with $1”.
- If a measurement is ratio scaled, we can speak of a value as being a multiple (or ratio) of another value.
The central tendency of an ratio attribute can be represented by its mode, its median (middle value in an ordered sequence), and its mean
Properties of Attribute ValuesThe type of an attribute depends on which of the following properties it possesses:
- Distinctness: =, !=
- Order: < >
- Addition: + -
- Multiplication: * /
Nominal attribute: distinctness
Ordinal attribute: distinctness & order
Interval attribute: distinctness, order & addition
Ratio attribute: all 4 properties
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