It means that there’ll be plenty of job scope in AI and ML, and since Data Mining is an integral part of both, you must build a solid foundation in Data Mining. Data Mining refers to the technique used to convert raw data into meaningful insights that can be used by businesses and organizations. Some of the fundamental aspects of Data Mining include data & database management, data pre-processing, data validation, online updating, and discovery of valuable patterns hidden within complex datasets. Essentially, Data Mining focuses on the automatic analysis of large volumes of data to extract the hidden trends and insights from it. This is precisely why you must be ready to answer any Data Mining question that the interviewer puts before you if you want to land your dream job in AI/ML.
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In this post, we’ve compiled a list of the most commonly asked Data Mining interview questions. It covers all levels of Data Mining interview questions and concepts (both basic and advanced levels) that every AI/ML aspirant must know.
So, without further delay, let’s get right into it!
- Name the different Data Mining techniques and explain the scope of Data Mining.
The different Data Mining techniques are:
- Prediction – It discovers the relationship between independent and dependent instances. For instance, when considering sales data, if you wish to predict the future profit, the sale acts as an independent instance, whereas the profit is the dependent instance. Accordingly, based on the historical data of sales and profit, the associated profit is predicted value.
- Decision trees – The root of a decision tree functions as a condition/question having multiple answers. Each answer leads to specific data that helps in determining the final decision based on the data.
- Sequential patterns – It refers to the pattern analysis used for discovering identical patterns in transaction data or regular events. For example, historical data of customers helps a brand to identify the patterns in the transactions that happened in the past year.
- Clustering analysis – In this technique, automatically a cluster of objects having similar characteristics is formed. Clustering method defines classes and then places suitable objects in each class.
- Classification analysis – In this ML-based method, each item in a particular set is classified into predefined groups. It uses advanced techniques like linear programming, neural networks, decision trees, etc.
- Association rule learning – This method creates a pattern based on the relationship of the items in a single transaction.
The scope of Data Mining is to:
- Predict trends and behaviours – Data Mining automates the process of identifying predictive information in large datasets/databases.
- Discover previously unknown patterns – Data Mining tools sweep and scrape through a broad and diverse range of databases to identify the previously hidden trends. This is nothing but a pattern discovery process.
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- What are the types of Data Mining?
Data Mining can be classified into the following types:
- Data cleaning
- Pattern evaluation
- Data transformation
- Knowledge representation
- What is Data Purging?
Data Purging is a crucial procedure in database management systems. It helps to maintain relevant data in a database. It refers to the process of cleaning junk data by eliminating or deleting the unnecessary NULL values of row and columns. Whenever you need to load new data in the database, first, it is essential to purge the irrelevant data.
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With frequent Data Purging of the database, you can get rid of the junk data that takes up a substantial amount of database memory, thereby slowing down the performance of the database.
- What is the fundamental difference between Data Warehousing and Data Mining?
Data Warehousing is the technique used for extracting data from disparate sources. It is then cleaned and stored for future use. On the other hand, Data Mining is the process of exploring the extracted data using queries and then analyze the results or outcomes. It is essential in reporting, strategy planning, and visualizing the valuable insights within the data.
- Explain the different stages of Data Mining.
There are three main stages of Data Mining:
Exploration – This stage is primarily focused on collecting data from multiple sources and preparing it for further activities like cleaning and transformation. Once the data is cleaned and transformed, it can be analyzed for insights.
Model Building and validation – This stage involves validating the data by applying different models to it and comparing the results for best performance. This step is also called as pattern identification. It is a time-consuming process since the user has to manually identify which pattern is the best suited for easy predictions.
Deployment – Once the bests-suited pattern for prediction is identified, it is applied to the dataset for obtaining estimated predictions or outcomes.
- What is the use of Data Mining queries?
Data Mining queries help facilitate the application of the model to the new data, either to make single or multiple results. Queries can retrieve cases that fit a particular pattern more effectively. They extract the statistical memory of the training data and help in obtaining the exact pattern along with the rule of the typical case that represents a pattern in the model. Furthermore, queries can extract regression formulas and other calculations to explain patterns. They can also retrieve the details about the individual cases used in a model.
- What are “Discrete” and “Continuous” data in Data Mining?
In Data Mining, discrete data is the data that is finite and has a meaning attached to it. Gender is a classic example of discrete data. Continuous data, on the other hand, is the data that continues to change in a well-structured manner. Age is a perfect example of continuous data.
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- What is OLAP? How is it different from OLTP?
OLAP (Online Analytical Processing) is a technology used in many Business Intelligence applications that involve complex analytical calculations. Apart from complex computations, OLAP is used for trends analysis and advanced data modelling. The primary purpose of using OLAP systems is to minimize the query response time while simultaneously boosting the effectiveness of reporting. The OLAP database stores aggregated historical data in a multidimensional schema. Being a multidimensional database, OLAP allows a user to understand how the data is coming through different sources.
OLTP stands for Online Transaction and Processing. It is inherently different from OLAP since it is used in applications that involve bulk transactions and large volumes of data. These applications are primarily found in the BFSI sector. OLTP architecture is a client-server architecture that can support cross-network transactions.
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- Name the different storage models that are available in OLAP?
The different storage models available in OLAP are:
- MOLAP (Multidimensional Online Analytical Processing) – This is a type of data storage where the data is stored in multidimensional cubes instead of standard relational databases. It is this feature which makes the query performance excellent.
- ROLAP (Relational Online Analytical Processing) – In this data storage, the data is stored in relational databases, and hence, it is capable of handling a vast volume of data.
- HOLAP (Hybrid Online Analytical Processing) – This is a combination of MOLAP and ROLAP. HOLAP uses the MOLAP model to extract summarized information from the cube, whereas for drill-down capabilities, it uses the ROLAP model.
- What is “Cube?”
In Data Mining, the term “cube” refers to a data storage space where data is stored. Storing data in a cube helps expedite the process of data analysis. Essentially, cubes are the logical representation of multidimensional data. While the edge of the cube has the dimension members, the body of the cube contains the data values.
Let’s assume that a company stores its employee data (records) in a cube. When it wishes to evaluate the employee performance based on a weekly or monthly basis, then the week/month becomes the dimensions of the cube.
- What is Data Aggregation and Generalization?
Data Aggregation is the process wherein the data is combined or aggregated together to create a cube for data analysis. Generalization is the process of replacing the low-level data with high-level concepts so that the data can be generalized and produce meaningful insights.
- Explain the Decision Tree and Time Series algorithms.
In the Decision Tree algorithm, each node is either a leaf node or a decision node. Every time you input an object in the algorithm, it produces a decision. A Decision Tree is created using the regularities of the data. All the paths connecting the root node to the leaf node are reached either by using ‘AND’ or ‘OR’ or ‘BOTH.’ It is important to note that the Decision Tree remains unaffected by Automatic Data Preparation.
The Time-Series algorithm is used for data types whose values keep changing continually based on time (for example, a person’s age). When you trained the algorithm and tune it to predict the dataset, it can successfully keep track of the continuous data and make accurate predictions. The Time-Series algorithm creates a specific model that can predict the future trends of the data based on the original dataset.
- What is clustering?
In Data Mining, clustering is the process used to group abstract objects into classes containing similar objects. Here, a cluster of data objects is treated as one group. Thus, during the analysis process, data partition happens in groups which are then labelled based on identical data. Cluster analysis is pivotal to Data Mining because it is highly scalable and dimensional, and it can also deal with different attributes, interpretability, and messy data.
Data clustering is used in several applications, including image processing, pattern recognition, fraud detection, and market research.
- What are the common issues faced during Data Mining?
During the Data Mining process, you can encounter the following issues:
- Uncertainty handling
- Dealing with missing values
- Dealing with noisy data
- Efficiency of algorithms
- Incorporating domain knowledge
- Size and complexity of data
- Data selection
- Inconsistency between the data and discovered knowledge.
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- Specify the syntax for – Interestingness Measures Specification, Pattern Presentation and Visualization Specification, and Task-Relevant Data Specification.
The syntax for Interestingness Measures Specification is:
with <interest_measure_name> threshold = threshold_value
The syntax for Pattern Presentation and Visualization Specification is:
display as <result_form>
The syntax for Task-Relevant Data Specification is:
use database database_name
use data warehouse data_warehouse_name
in relevance to att_or_dim_list
from relation(s)/cube(s) [where condition] order by order_list
group by grouping_list
- Name the different level of analysis in Data Mining?
The various levels of analysis in Data Mining are:
- Rule induction
- Data visualization
- Genetic algorithms
- Artificial neural network
- Nearest neighbour method
- What is STING?
STING stands for Statistical Information Grid. It is a grid-based, multi-resolution clustering method in which all the objects are contained into rectangular cells. While the cells are kept in various levels of resolutions, these levels are further arranged in a hierarchical structure.
- What is ETL? Name some of the best ETL tools.
ETL stands for Extract, Transform and Load. It is a software that can read the data from the specified data source and extract a desired subset of data. After this, it transforms the data using rules and lookup tables and converts it to the desired form. Finally, it uses the load function to load the resulting data into the target database.
The best ETL tools are:
- Ab Initio
- Data Stage
- Data Junction
- Warehouse Builder
- What is Metadata?
In simple words, metadata is the summarized data that leads to the larger dataset. Metadata contains important information like the number of columns used, the order of the fields, the data types of the fields, fix width and limited width, and so on.
- What are the advantages of Data Mining?
Data Mining has four core advantages:
- It helps make sense of raw data and explore, identify, and understand the patterns hidden within the data.
- It helps automates the process of finding predictive information in large databases, thereby helping to promptly identify the previously hidden patterns.
- It helps to screen and validate the data and understand where it is coming from.
- It promotes faster and better decision making, thereby helping businesses to take necessary actions to increase revenue and lower operational costs.
These are the reasons why Data Mining has become an integral part of numerous industries, including marketing, advertising, IT/ITES, business intelligence, and even government intelligence.
We hope these Data Mining interview questions and their answers help you to break the ice with Data Mining. Although these are just a few basic level questions you must know, they will help you to get in the flow and dig deeper into the subject matter.
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What are the drawbacks of using a decision tree algorithm?
Even a minor change in the data can cause a significant change in the structure of the decision tree, resulting in instability. When compared to other algorithms, the calculation of a decision tree might be rather complex at times. Decision tree training is relatively expensive due to the complexity and time required. The Decision Tree technique fails when it comes to applying regression and predicting continuous values.
What is the difference between data mining clustering and classification?
Clustering is a technique of unsupervised learning, whereas classification is a way of supervised learning. Clustering is the process of grouping data points into clusters based on their commonalities. Classification entails labelling the input data with one of the output variable's class labels. Clustering splits the dataset into subgroups, allowing examples with similar functionality to be grouped together. It doesn't rely on labelled data or a training set to work. Classification, on the other hand, classifies new data based on observations from the training set.
Are there any disadvantages of data mining?
Many privacy problems arise when data mining is used. Despite the fact that data mining has opened the path for simple data collection in its own way. When it comes to precision, it still has certain limits. The data obtained might be incorrect, producing issues with decision-making. The data collecting procedure for data mining uses a lot of technology. Every piece of data created requires its own storage and upkeep. The cost of implementation might skyrocket as a result of this.