lec 2(done) Flashcards

1
Q

Data Mining Functionalities:

A

1-Class/concept description
2-Mining frequent patterns, associations, and correlations
3-Classification and regression for predictive analysis
4-Cluster analysis
5-Outlier analysis

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2
Q

Data characterization:

A

Summarization of the general characteristics or features of a target class of data.

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3
Q

Data discrimination:

A

Comparison of the general features of the target class against one or a set of contrasting classes.

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4
Q

Frequent Patterns:

A

patterns that occur frequently in data.

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5
Q

Association Analysis:

A

Mining frequent patterns leads to the discovery of interesting associations and correlations within data.

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6
Q

Frequent Patterns and Associations applications:

A

1-Marketing and Sales Promotion.
2-Supermarket shelf management.
3-Inventory Management.

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7
Q

Classification:

A

Construct a model (function) based on some training examples to describe and distinguish data classes or concepts for future prediction.

Classification predicts categorical (discrete) labels.

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8
Q

Typical methods for data classification:

A

Decision trees, naïve Bayesian classification, support vector machines, neural networks, classificationrules (i.e., IF-THEN rules), logistic regression, …

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9
Q

Regression

A

is used to predict numerical (continuous) values.

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10
Q

Applications of Classification and Prediction:

A

Credit card fraud detection, direct marketing, classifying diseases..
Predicting wind velocity, temperature, sales amount of a product, stock market,…

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11
Q

Cluster analysis:

A
  • Unsupervised learning (Class label is unknown)

- Group data to form new categories (i.e., clusters)

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12
Q

Cluster analysis Applications:

A

1-Cluster houses to find distribution patterns.

2-Document clustering.

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13
Q

Outlier:

A

A data object that does not comply with the general behavior of the data (noise or exception)

Useful in fraud detection, rare events analysis

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14
Q

Major Issues in Data Mining:

A

1-Mining Methodology:
-Mining various and new kinds of knowledge.
-Mining knowledge in multi-dimensional space.
-Data mining: An interdisciplinary effort.
-Handling noise, uncertainty, and incompleteness of data.
-Pattern evaluation.
2-User Interaction:
-Incorporation of background knowledge.
-Presentation and visualization of data mining results.

3-Efficiency and Scalability:

  • Efficiency and scalability of data mining algorithms.
  • Parallel, distributed, and incremental mining methods.

4-Diversity of data types:

  • Handling complex types of data.
  • Mining dynamic, networked, and global data repositories.

5-Data mining and society:

  • Social impacts of data mining.
  • Privacy-preserving data mining.
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