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Tasks and Functionalities of Data Mining

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Data Mining functions are used to define the trends or correlations contained in data mining activities. In comparison, data mining activities can be divided into 2 categories:

1]Descriptive Data Mining: 

This category of data mining is concerned with finding patterns and relationships in the data that can provide insight into the underlying structure of the data. Descriptive data mining is often used to summarize or explore the data, and it can be used to answer questions such as: What are the most common patterns or relationships in the data? Are there any clusters or groups of data points that share common characteristics? What are the outliers in the data, and what do they represent?
Some common techniques used in descriptive data mining include:

Cluster analysis: 

This technique is used to identify groups of data points that share similar characteristics. Clustering can be used for segmentation, anomaly detection, and summarization.

Association rule mining: 

This technique is used to identify relationships between variables in the data. It can be used to discover co-occurring events or to identify patterns in transaction data.

Visualization: 

This technique is used to represent the data in a visual format that can help users to identify patterns or trends that may not be apparent in the raw data.

2]Predictive Data Mining: This category of data mining is concerned with developing models that can predict future behavior or outcomes based on historical data. Predictive data mining is often used for classification or regression tasks, and it can be used to answer questions such as: What is the likelihood that a customer will churn? What is the expected revenue for a new product launch? What is the probability of a loan defaulting?
Some common techniques used in predictive data mining include:

Decision trees: This technique is used to create a model that can predict the value of a target variable based on the values of several input variables. Decision trees are often used for classification tasks.

Neural networks: This technique is used to create a model that can learn to recognize patterns in the data. Neural networks are often used for image recognition, speech recognition, and natural language processing.

Regression analysis: This technique is used to create a model that can predict the value of a target variable based on the values of several input variables. Regression analysis is often used for prediction tasks.

Both descriptive and predictive data mining techniques are important for gaining insights and making better decisions. Descriptive data mining can be used to explore the data and identify patterns, while predictive data mining can be used to make predictions based on those patterns. Together, these techniques can help organizations to understand their data and make informed decisions based on that understanding.

Data Mining Functionality: 

1. Class/Concept Descriptions: Classes or definitions can be correlated with results. In simplified, descriptive and yet accurate ways, it can be helpful to define individual groups and concepts. These class or concept definitions are referred to as class/concept descriptions.

  • Data Characterization: This refers to the summary of general characteristics or features of the class that is under the study. The output of the data characterization can be presented in various forms include pie charts, bar charts, curves, multidimensional data cubes.

Example: To study the characteristics of software products with sales increased by 10% in the previous years. To summarize the characteristics of the customer who spend more than $5000 a year at AllElectronics, the result is general profile of those customers such as that they are 40-50 years old, employee and have excellent credit rating.

  • Data Discrimination: It compares common features of class which is under study. It is a comparison of the general features of the target class data objects against the general features of objects from one or multiple contrasting classes.

Example: we may want to compare two groups of customers those who shop for computer products regular and those who rarely shop for such products(less than 3 times a year), the resulting description provides a general comparative profile of those customers, such as 80% of the customers who frequently purchased computer products are between 20 and 40 years old and have a university degree, and 60% of the customers who infrequently buys such products are either seniors or youth, and have no university degree.

2. Mining Frequent Patterns, Associations, and Correlations: Frequent patterns are nothing but things that are found to be most common in the data. There are different kinds of frequencies that can be observed in the dataset.

  • Frequent item set: This applies to a number of items that can be seen together regularly for eg: milk and sugar.
  • Frequent Subsequence: This refers to the pattern series that often occurs regularly such as purchasing a phone followed by a back cover.
  • Frequent Substructure: It refers to the different kinds of data structures such as trees and graphs that may be combined with the itemset or subsequence.

Association Analysis: The process involves uncovering the relationship between data and deciding the rules of the association. It is a way of discovering the relationship between various items.

Example: Suppose we want to know which items are frequently purchased together. An example for such a rule mined from a transactional database is,

buys (X, “computer”) ⇒ buys (X, “software”) [support = 1%, confidence = 50%],

where X is a variable representing a customer. A confidence, or certainty, of 50% means that if a customer buys a computer, there is a 50% chance that she will buy software as well. A 1% support means that 1% of all the transactions under analysis show that computer and software are purchased together. This association rule involves a single attribute or predicate (i.e., buys) that repeats. Association rules that contain a single predicate are referred to as single-dimensional association rules.

age (X, “20…29”) ∧ income (X, “40K..49K”) ⇒ buys (X, “laptop”)

[support = 2%, confidence = 60%].

The rule says that 2% are 20 to 29 years old with an income of $40,000 to $49,000 and have purchased a laptop. There is a 60% probability that a customer in this age and income group will purchase a laptop. The association involving more than one attribute or predicate can be referred to as a multidimensional association rule.

Typically, association rules are discarded as uninteresting if they do not satisfy both a minimum support threshold and a minimum confidence threshold. Additional analysis can be performed to uncover interesting statistical correlations between associated attribute–value pairs.

Correlation Analysis: Correlation is a mathematical technique that can show whether and how strongly the pairs of attributes are related to each other. For example, Highted people tend to have more weight.

Data Mining Task Primitives

Data mining task primitives refer to the basic building blocks or components that are used to construct a data mining process. These primitives are used to represent the most common and fundamental tasks that are performed during the data mining process.  The use of data mining task primitives can provide a modular and reusable approach, which can improve the performance, efficiency, and understandability of the data mining process.  

The Data Mining Task Primitives are as follows:

  1. The set of task relevant data to be mined: It refers to the specific data that is relevant and necessary for a particular task or analysis being conducted using data mining techniques. This data may include specific attributes, variables, or characteristics that are relevant to the task at hand, such as customer demographics, sales data, or website usage statistics. The data selected for mining is typically a subset of the overall data available, as not all data may be necessary or relevant for the task.  For example: Extracting the database name, database tables, and relevant required attributes from the dataset from the provided input database.
  2. Kind of knowledge to be mined: It refers to the type of information or insights that are being sought through the use of data mining techniques. This describes the data mining tasks that must be carried out. It includes various tasks such as classification, clustering, discrimination, characterization, association, and evolution analysis. For example, It determines the task to be performed on the relevant data in order to mine useful information such as classification, clustering, prediction, discrimination, outlier detection, and correlation analysis.  
  3. Background knowledge to be used in the discovery process: It refers to any prior information or understanding that is used to guide the data mining process. This can include domain-specific knowledge, such as industry-specific terminology, trends, or best practices, as well as knowledge about the data itself. The use of background knowledge can help to improve the accuracy and relevance of the insights obtained from the data mining process. For example, The use of background knowledge such as concept hierarchies, and user beliefs about relationships in data in order to evaluate and perform more efficiently.  
  4. Interestingness measures and thresholds for pattern evaluation: It refers to the methods and criteria used to evaluate the quality and relevance of the patterns or insights discovered through data mining. Interestingness measures are used to quantify the degree to which a pattern is considered to be interesting or relevant based on certain criteria, such as its frequency, confidence, or lift. These measures are used to identify patterns that are meaningful or relevant to the task. Thresholds for pattern evaluation, on the other hand, are used to set a minimum level of interestingness that a pattern must meet in order to be considered for further analysis or action.  For example: Evaluating the interestingness and interestingness measures such as utility, certainty, and novelty for the data and setting an appropriate threshold value for the pattern evaluation.   
  5. Representation for visualizing the discovered pattern: It refers to the methods used to represent the patterns or insights discovered through data mining in a way that is easy to understand and interpret. Visualization techniques such as charts, graphs, and maps are commonly used to represent the data and can help to highlight important trends, patterns, or relationships within the data. Visualizing the discovered pattern helps to make the insights obtained from the data mining process more accessible and understandable to a wider audience, including non-technical stakeholders. For example Presentation and visualization of discovered pattern data using various visualization techniques such as barplot, charts, graphs, tables, etc.

Advantages of Data Mining Task Primitives  

The use of data mining task primitives has several advantages, including:

  1. Modularity: Data mining task primitives provide a modular approach to data mining, which allows for flexibility and the ability to easily modify or replace specific steps in the process. 
  2. Reusability: Data mining task primitives can be reused across different data mining projects, which can save time and effort. 
  3. Standardization: Data mining task primitives provide a standardized approach to data mining, which can improve the consistency and quality of the data mining process. 
  4. Understandability: Data mining task primitives are easy to understand and communicate, which can improve collaboration and communication among team members. 
  5. Improved Performance: Data mining task primitives can improve the performance of the data mining process by reducing the amount of data that needs to be processed, and by optimizing the data for specific data mining algorithms. 
  6. Flexibility: Data mining task primitives can be combined and repeated in various ways to achieve the goals of the data mining process, making it more adaptable to the specific needs of the project.  
  7. Efficient use of resources: Data mining task primitives can help to make more efficient use of resources, as they allow to perform specific tasks with the right tools, avoiding unnecessary steps and reducing the time and computational power needed.  


Last Updated : 22 Aug, 2023
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