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On the basis of presence and absence of an attribute, data are classified into
- a)one fold classification.
- b)two fold classification.
- c)three fold classification.
- d)four fold classification.
Correct answer is option 'B'. Can you explain this answer?
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On the basis of presence and absence of an attribute, data are classif...
When on the basis presence and absence of an attribute, the data are classified into two classes, one possessing that attribute and the other not possessing that attribute, it is called two-fold classification.
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On the basis of presence and absence of an attribute, data are classif...
Two-fold Classification of Data based on the presence and absence of an attribute
Data classification is the process of organizing and categorizing data into various groups or classes based on certain characteristics or attributes. One common way of classifying data is based on the presence and absence of a particular attribute. In this classification, data can be divided into two groups - one group includes data that possess the attribute, and the other group includes data that do not possess the attribute.
Explanation:
To understand the two-fold classification of data based on the presence and absence of an attribute, let's consider an example. Suppose we have a dataset of employees in a company, and we want to classify them based on their gender. The attribute here is "gender" which can have two possible values - male and female.
1. Presence of Attribute:
In this case, the data that possess the attribute (gender) are classified into one group. For example, all the female employees in the dataset would be grouped together as they possess the attribute "female". This group represents the presence of the attribute.
2. Absence of Attribute:
On the other hand, the data that do not possess the attribute (gender) are classified into another group. In our example, all the male employees in the dataset would be grouped together as they do not possess the attribute "female". This group represents the absence of the attribute.
Advantages of Two-fold Classification:
- Simplicity: The two-fold classification is straightforward and easy to understand as it involves only two groups.
- Clear Distinction: It clearly distinguishes between data that possess the attribute and data that do not possess the attribute.
- Efficient Analysis: This classification method allows for efficient analysis and comparison of data based on the presence or absence of an attribute.
Conclusion:
The two-fold classification of data based on the presence and absence of an attribute is a simple and effective way of organizing and categorizing data. It allows for clear distinction and efficient analysis, making it a valuable tool for data classification in various domains.
Data classification is the process of organizing and categorizing data into various groups or classes based on certain characteristics or attributes. One common way of classifying data is based on the presence and absence of a particular attribute. In this classification, data can be divided into two groups - one group includes data that possess the attribute, and the other group includes data that do not possess the attribute.
Explanation:
To understand the two-fold classification of data based on the presence and absence of an attribute, let's consider an example. Suppose we have a dataset of employees in a company, and we want to classify them based on their gender. The attribute here is "gender" which can have two possible values - male and female.
1. Presence of Attribute:
In this case, the data that possess the attribute (gender) are classified into one group. For example, all the female employees in the dataset would be grouped together as they possess the attribute "female". This group represents the presence of the attribute.
2. Absence of Attribute:
On the other hand, the data that do not possess the attribute (gender) are classified into another group. In our example, all the male employees in the dataset would be grouped together as they do not possess the attribute "female". This group represents the absence of the attribute.
Advantages of Two-fold Classification:
- Simplicity: The two-fold classification is straightforward and easy to understand as it involves only two groups.
- Clear Distinction: It clearly distinguishes between data that possess the attribute and data that do not possess the attribute.
- Efficient Analysis: This classification method allows for efficient analysis and comparison of data based on the presence or absence of an attribute.
Conclusion:
The two-fold classification of data based on the presence and absence of an attribute is a simple and effective way of organizing and categorizing data. It allows for clear distinction and efficient analysis, making it a valuable tool for data classification in various domains.
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On the basis of presence and absence of an attribute, data are classified intoa)one fold classification.b)two fold classification.c)three fold classification.d)four fold classification.Correct answer is option 'B'. Can you explain this answer?
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