Group A

1. Data mining and Data warehousing Brief Answer Questions.

1. Mention any two data mining techniques.

  1. Classification: This is a technique used to categorize data into different classes or groups based on their attributes or characteristics. It involves building a model that can predict the class of new data instances based on their attributes. Common algorithms for classification include Decision Trees, Neural Networks, and Support Vector Machines (SVMs).
  2. Clustering: This is a technique used to group data objects or instances into clusters based on their similarity or distance from each other. It involves finding patterns in the data without any prior knowledge of the groups. Common clustering algorithms include K-Means, Hierarchical Clustering, and Density-Based Spatial Clustering of Applications with Noise (DBSCAN).

2. what is an attribute selection measure in a decision tree?

In decision tree algorithms, an attribute selection measure is used to determine the importance or relevance of an attribute (feature) in the classification or prediction task. It is used to choose the best attribute for splitting the data at each node of the tree.

There are several attribute selection measures used in decision tree algorithms, including:

  1. Information gain
  2. Gain Ratio
  3. Gini index
  4. Chi-squared

3. How do you validate the classification model?

Validation of a classification model is an important step in the data mining process. There are several techniques for validating the performance of a classification model, including:

  1. Cross-validation
  2. Holdout method
  3. Leave-one-out cross-validation
  4. Bootstrapping

4. what are the different types of data used for cluster analysis?

Cluster analysis is a technique used in data mining to group similar objects or data points into clusters based on their attributes or characteristics. The type of data used for cluster analysis depends on the problem at hand and the type of variables involved. The different types of data used for cluster analysis are:

  1. Interval or Ratio Data
  2. Ordinal Data
  3. Nominal data
  4. Binary Data
  5. Text Data

5. Define base cuboid.

In data mining and data warehousing, a base cuboid is the lowest level of aggregation in a data cube. It is the initial data set that is used to construct the data cube, and contains the raw, detailed data without any summarization or aggregation.

6. List the advantages of MOLAP.

  1. Fast Query Performance
  2. Complex Data Analysis
  3. Intuitive Interface
  4. Aggregation and Rollup
  5. Data Consistency
  6. Scalability
  7. Flexibility

7. Mention the purpose of FP tree

The purpose of the FP tree is to store the itemsets in a compact and easily retrievable form so that frequent itemsets can be generated efficiently.

8. List any two pitfalls of data mining

  • Overfitting
  • Data quality issues

9. Give any two applications of web mining.

Web mining is the process of discovering useful information from the contents, structure, and usage of web data. It has many applications in various fields. Here are two examples of web mining applications:

  1. Personalized recommendation system
  2. Search engine optimization

10. Differentiate agglomerative and divisive hierarchical clustering

Agglomerative clustering: Agglomerative clustering is a bottom-up approach to hierarchical clustering, where each data point starts as its own cluster, and clusters are successively merged until a desired number of clusters is reached.

Divisive clustering: Divisive clustering is a top-down approach to hierarchical clustering, where all data points start as a single cluster, and clusters are successively split until a desired number of clusters is reached.

Group B

Data mining and Data warehousing Exercise Problems.

2. If epsilon = 2 and Minpts = 2, what are core point, boarder point and outliner that DBSCAN would find from the data set A (3, 10), B (2, 3), C (3, 4), D(6, 7) and E (7, 6)

To determine the core points, border points, and outliers using DBSCAN clustering algorithm with epsilon = 2 and MinPts = 2 on the given dataset, we need to calculate the distance between each point and count the number of points within the epsilon radius of each point. Here are the steps:

  • Calculate the distance between each point and other points in the dataset:
Point A B C D E
A 0
B 0
C 0
D 0
E 0
  • Count the number of points within the epsilon radius of each point:
    • A: 3 (B, C, D)
    • B: 2 (A, C)
    • C: 2 (A, B)
    • D: 1 (A)
    • E: 0 (no points within epsilon radius)
  • Identify the core points, border points, and outliers:
    • Core points: A, C, D (have at least MinPts points within epsilon radius)
    • Border points: B (have less than MinPts points within epsilon radius but are reachable from core points)
    • Outliers: E (not a core point and not reachable from core points)

Therefore, using DBSCAN clustering algorithm with epsilon = 2 and MinPts = 2 on the given dataset, the core points are A, C, and D, the border point is B, and the outlier is E.

3. Given the following transaction set, find the frequent itemset using Apriori algorithm.

Transaction -> Items
T1: {pasta, lemon, bread, orange}
T2: {pasta, lemon}
T3: {pasta, orange, cake}
T4: {pasta, lemon, orange, cake}
minimum support=2

To find the frequent itemset using the Apriori algorithm with a minimum support of 2 on the given transaction set, we need to perform the following steps:

Step 1: Generate the candidate itemsets

In the first iteration, we generate the candidate itemsets of size 1, which are simply the individual items in the transaction set. We count the support of each candidate itemset by scanning the transaction set and counting the number of transactions containing each item. The frequent itemsets at this stage are those that have a support of at least 2, which is the minimum support.

Itemset Support
{pasta} 4
{lemon} 3
{bread} 1
{orange} 3
{cake} 2

Step 2: Generate the candidate itemsets of size 2

In the second iteration, we generate the candidate itemsets of size 2 by joining the frequent itemsets from the previous iteration. We count the support of each candidate itemset by scanning the transaction set and counting the number of transactions containing the candidate itemset. The frequent itemsets at this stage are those that have a support of at least 2, which is the minimum support.

Itemset Support
{pasta, lemon} 2
{pasta, orange} 3

Step 3: Generate the candidate itemsets of size 3

In the third iteration, we generate the candidate itemsets of size 3 by joining the frequent itemsets from the previous iteration. We count the support of each candidate itemset by scanning the transaction set and counting the number of transactions containing the candidate itemset. Since there are no frequent itemsets of size 3 that have a support of at least 2, we stop here and return the frequent itemsets found so far.

Itemset Support
{pasta} 4
{lemon} 3
{orange} 3
{pasta, lemon} 2
{pasta, orange} 3

Therefore, the frequent itemsets with a minimum support of 2 found in the transaction set are {pasta}, {lemon}, {orange}, {pasta, lemon}, and {pasta, orange}.

4. Illustrate the significance of authorities and hub in ranking the web pages.

The authority and hub concepts are used in the HITS (Hyperlink-Induced Topic Search) algorithm, which is a link analysis algorithm used for ranking web pages. The basic idea behind HITS is that a good web page is one that is linked to by other good web pages, and that a good web page also links to other good web pages.

In HITS, each web page is assigned two scores: an authority score and a hub score. The authority score measures how important a web page is in terms of content, while the hub score measures how important a web page is in terms of links to other pages.

The authority score of a web page is based on the number and quality of inbound links it receives from other web pages. If a web page has many high-quality inbound links, it is considered to be a good authority. The authority score of a web page is computed by summing up the hub scores of the web pages that link to it.

The hub score of a web page is based on the number and quality of outbound links it has to other web pages. If a web page has many high-quality outbound links, it is considered to be a good hub. The hub score of a web page is computed by summing up the authority scores of the web pages it links to.

The authority and hub scores of each web page are updated iteratively until they converge to a stable value. The final authority and hub scores are used to rank the web pages, with the highest authority and hub scores being the most important web pages.

The significance of the authority and hub concepts in ranking web pages is that they capture both the content and the link structure of the web. By considering both the inbound links to a web page and the outbound links from a web page, HITS is able to capture the notion of “goodness” of a web page in a holistic way. This is in contrast to other ranking algorithms, such as PageRank, which only consider the inbound links to a web page.

Also Read: Data Mining and Data warehousing | BIM 8th Semester TU Solution | 2017

5. What is an operational data source? list some guidelines to be considered in data warehouse implementation.

An operational data source is a system that collects, stores, and manages data that is used to support the daily operations of an organization. Examples of operational data sources include databases, transaction processing systems, and customer relationship management systems.

Data warehouse implementation is a complex process that requires careful planning and execution. Here are some guidelines to consider when implementing a data warehouse:

  1. Define clear business goals and objectives: Before embarking on a data warehouse implementation, it is important to define the business goals and objectives that the data warehouse is intended to support.
  2. Plan for data quality: Data quality is critical to the success of a data warehouse. It is important to plan for data quality from the outset, including data cleansing and validation processes.
  3. Select an appropriate data model: A data model is the framework that defines the structure and relationships of the data in the warehouse. It is important to select an appropriate data model that supports the business requirements and data analysis needs.
  4. Establish a data integration strategy: Data integration is the process of combining data from multiple sources into a unified view. It is important to establish a data integration strategy that ensures the data in the warehouse is accurate and up-to-date.
  5. Define security and access controls: Security and access controls are critical to protecting the data in the warehouse. It is important to define appropriate security and access controls to ensure that only authorized users have access to the data.
  6. Plan for scalability and performance: A data warehouse must be able to scale to meet the changing needs of the business. It is important to plan for scalability and performance from the outset, including selecting appropriate hardware and software.
  7. Establish a data governance framework: Data governance is the process of managing the availability, usability, integrity, and security of the data in the warehouse. It is important to establish a data governance framework that ensures the data in the warehouse is managed effectively.

6. Assume the following training set with two classes, Food and Beverage.
Food: “turkey stuffing”
Food: “buffalo wings”
Beverage: “cream soda”
Beverage: “orange soda”
Apply k-Nearest neighbor with K=3 to classify the new document “turkey soda”.

To classify the new document “turkey soda” using k-Nearest Neighbor (k-NN) with k=3, we need to find the three closest neighbors to “turkey soda” from the training set and assign a class to the new document based on the majority class among its three nearest neighbors.

First, we need to convert the text data into a numerical representation using a text vectorization technique such as Bag-of-Words or TF-IDF. Let’s assume we use Bag-of-Words and represent each document as a vector of term frequencies, ignoring case and punctuation:

Food: “turkey stuffing” -> [1, 0, 1, 0] (the vocabulary is [“turkey”, “stuffing”, “buffalo”, “wings”])

Food: “buffalo wings” -> [0, 1, 1, 1]

Beverage: “cream soda” -> [0, 0, 1, 1]

Beverage: “orange soda” -> [0, 0, 1, 1]

New document: “turkey soda” -> [1, 0, 0, 0]

Next, we need to calculate the distance between the new document and each document in the training set. We can use Euclidean distance or cosine similarity as the distance metric. Let’s use Euclidean distance:

  1. Distance to “turkey stuffing”: sqrt((1-1)^2 + (0-0)^2 + (1-0)^2 + (0-1)^2) = sqrt(3) = 1.732
  2. Distance to “buffalo wings”: sqrt((0-1)^2 + (1-0)^2 + (1-1)^2 + (1-0)^2) = sqrt(4) = 2
  3. Distance to “cream soda”: sqrt((0-1)^2 + (0-0)^2 + (1-0)^2 + (1-0)^2) = sqrt(3) = 1.732
  4. Distance to “orange soda”: sqrt((0-1)^2 + (0-0)^2 + (1-0)^2 + (1-0)^2) = sqrt(3) = 1.732

The three nearest neighbors to “turkey soda” are “turkey stuffing”, “cream soda”, and “orange soda”, all with a distance of sqrt(3).

Therefore, we assign the majority class among the three nearest neighbors to “turkey soda”. Since two of the neighbors are from the Beverage class and one is from the Food class, we classify “turkey soda” as Beverage.

Also Read: Data Mining | BIM 8th Semester | TU Solution -2018

Group C

Data mining and Data warehousing Comprehensive Questions

7. Define the dimension table. List the responsibility of query manager.

A dimension table is a table in a data warehouse that stores information about the dimensions of the data being analyzed. A dimension is a categorical attribute of the data, such as time, location, product, or customer. Each row in the dimension table represents a unique value of a dimension attribute, and it contains additional descriptive information about the dimension, such as the name, description, hierarchy, and relationships with other dimensions.

For example, a time dimension table may contain columns such as date, month, quarter, year, day of week, holiday, and season, and it may be related to other dimension tables such as product, store, and customer.

The responsibilities of a query manager in a data warehouse include:

  1. Managing and optimizing queries: The query manager is responsible for optimizing the performance of queries against the data warehouse. This involves monitoring the query workload, identifying and resolving performance bottlenecks, tuning the database parameters, and implementing indexing and partitioning strategies.
  2. Managing metadata: The query manager maintains the metadata repository that describes the structure and content of the data warehouse. This includes the schema definition, the data dictionary, the business rules, the relationships between tables, and the access privileges.
  3. Managing security: The query manager is responsible for ensuring the security and integrity of the data warehouse. This includes managing user accounts, roles, and permissions, enforcing data access policies, and auditing user activities.
  4. Managing concurrency: The query manager handles multiple concurrent queries against the data warehouse, ensuring that they don’t interfere with each other and that they are executed in a fair and efficient manner.
  5. Providing access to data: The query manager provides various interfaces for accessing the data in the data warehouse, such as SQL, OLAP, reporting tools, and dashboards. It also manages the data extraction, transformation, and loading (ETL) processes that populate the data warehouse from the operational systems.

8. What might be the cause of overfitting in classifier? Explain some data cube operation.

Overfitting in a classifier occurs when the model fits the training data too well, to the point of capturing the noise and outliers in the data. This results in a model that has high accuracy on the training data but poor generalization on new data. Some of the causes of overfitting in a classifier are:

  1. Insufficient training data: When the training data is too small or unrepresentative of the population, the model may learn the noise and outliers in the data instead of the underlying patterns.
  2. Overly complex model: When the model is too complex or has too many parameters, it may memorize the training data instead of learning the general patterns. This is often the case with decision trees and neural networks.
  3. Leakage of information: When the model has access to the target variable or some of the test data during training, it may learn to exploit this information and overfit the data.

Some of the common data cube operations are:

  1. Roll-up: This operation aggregates data along a dimension hierarchy, such as summing up the sales by month, quarter, and year.
  2. Drill-down: This operation disaggregates data from a higher-level summary to a lower-level detail, such as breaking down the sales by region, store, and product.
  3. Slice: This operation selects a subset of data that satisfies a condition on one or more dimensions, such as selecting the sales of a specific product in a specific region.
  4. Dice: This operation selects a subset of data that satisfies a condition on two or more dimensions, such as selecting the sales of a specific product in a specific region and month.
  5. Pivot: This operation transposes the data cube along one or more dimensions, such as converting the sales by product and month into sales by month and product.

These operations allow analysts to explore and analyze multidimensional data from different perspectives, to identify trends, anomalies, and opportunities, and to support decision-making at different levels of granularity and abstraction.

Also Read: Data mining and Data warehousing | TU Solution 2021


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