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Task1 Data Mining

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Data Mining

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Assiut University

Academic year: 2019/2020

Listed bookData Mining: Concepts and Techniques

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Name: Asmaa Mohammed

Dept : IT

• Attribute types

In data mining, attribute types refer to the different types of data that can be used to describe and analyze a dataset, Understanding the attribute types in a dataset is important for selecting appropriate data mining techniques and algorithms, as well as for interpreting the results of data analysis These attribute types can include: Nominal attributes: These are categorical variables that have no inherent order or ranking, such as gender or color, is in alphabetical form and not in an integer. Nominal Attributes are Qualitative Attributes : 1. ID numbers 2. Eye color (e. blue, brown, green) 3. Gender (e. male, female) 4. Marital status (e. single, married, divorced) 5. ZIP code

Properties / Operation of Nominal Attribute is :- Distinctness ( = , != ) 2. Ordinal attributes: These are categorical variables that have a natural ordering, such as education level or income bracket , an attribute with possible values that have a meaningful order or ranking among them, but the magnitude between successive values is not known : Ordinal data is a categorical, statistical data type where the variables have natural, ordered categories and the distances between the categories are not known

Customer satisfaction level (e. very dissatisfied, dissatisfied, neutral, satisfied, very satisfied)
Position in a race (1st place, 2nd place, 3rd place, etc. Properties / Operation of Ordinal Attribute is :- Distinctness( = , != ) & Order( < , > )
Interval attributes: These are numerical variables with a consistent scale and equal intervals between values, such as temperature or time, interval attributes are measured on a scale of equal-size units. We can compare and quantify the difference between values of interval attributes. Example: 1: A temperature attribute is an interval attribute. one temperature value as being a multiple of another.

Ratio: the data can be categorized, ranked, evenly spaced and has a natural zero

• Similarity and Dissimilarity

Distance or similarity measures are essential in solving many pattern recognition problems such as classification and clustering. Various distance/similarity measures are available in the literature to compare two data distributions. As the names suggest, a similarity measures how close two distributions are. For multivariate data complex summary methods are developed to answer this question. Similarity Measure Numerical measure of how alike two data objects often fall between 0 (no similarity) and 1 (complete similarity) Dissimilarity Measure Numerical measure of how different two data objects are range from 0 (objects are alike) to (objects are different).

Nominal is binary if two values are equal or not. Ordinal is the difference between two values, normalized by the maximum distance. Quantitative dissimilarity is just a distance between, similarity attempts to scale that distance to [0,1].

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