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Difference Between Descriptive and Predictive Data Mining
Descriptive mining:.
This term is basically used to produce correlation, cross-tabulation, frequency etc. These technologies are used to determine the similarities in the data and to find existing patterns. One more application of descriptive analysis is to develop the captivating subgroups in the major part of the data available. This analytics emphasis on the summarization and transformation of the data into meaningful information for reporting and monitoring.
Examples of descriptive data mining include clustering, association rule mining, and anomaly detection. Clustering involves grouping similar objects together, while association rule mining involves identifying relationships between different items in a dataset. Anomaly detection involves identifying unusual patterns or outliers in the data.
Predictive Data Mining:
The main goal of this mining is to say something about future results not of current behaviour. It uses the supervised learning functions which are used to predict the target value. The methods come under this type of mining category are called classification, time-series analysis and regression. Modelling of data is the necessity of the predictive analysis, and it works by utilizing a few variables of the present to predict the future not known data values for other variables.
Examples of predictive data mining include regression analysis, decision trees, and neural networks. Regression analysis involves predicting a continuous outcome variable based on one or more predictor variables. Decision trees involve building a tree-like model to make predictions based on a set of rules. Neural networks involve building a model based on the structure of the human brain to make predictions.
The main differences between descriptive and predictive data mining are:
Purpose: Descriptive data mining is used to describe the data and identify patterns and relationships. Predictive data mining is used to make predictions about future events.
Approach: Descriptive data mining involves analyzing historical data to identify patterns and relationships. Predictive data mining involves using statistical models and machine learning algorithms to identify patterns and relationships that can be used to make predictions.
Output: Descriptive data mining produces summaries and visualizations of the data. Predictive data mining produces models that can be used to make predictions.
Timeframe: Descriptive data mining is focused on analyzing historical data. Predictive data mining is focused on making predictions about future events.
Applications: Descriptive data mining is used in applications such as market segmentation, customer profiling, and product recommendation. Predictive data mining is used in applications such as fraud detection, risk assessment, and demand forecasting.
Difference Between Descriptive and Predictive Data Mining:
Conclusion:
In conclusion, descriptive and predictive data mining are two important techniques for discovering patterns and trends in large datasets. Descriptive data mining is used to summarize and describe the data, while predictive data mining is used to make predictions about future events. Both techniques have their own advantages and applications, and the choice of technique depends on the specific problem and the nature of the data.
Frequently Asked Questions:
Q: Can descriptive data mining be used to make predictions?
A: No, descriptive data mining is focused on describing and summarizing the data, and does not involve making predictions about future events.
Q: Can predictive data mining be used to describe the data?
A: Yes, predictive data mining involves analyzing the data to identify patterns and relationships that can be used to make predictions, which can also provide insights into the data.
Q: What are some examples of applications that use predictive data mining?
A: Some examples of applications that use predictive data mining include credit scoring, insurance
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Data mining task primitives.
Introduction: Each user will have a data mining task in mind, that is, some form of data analysis that he or she would like to have performed. A data mining task can be specified in the form of a data mining query, which is input to the data mining system. A data mining query is defined in terms of data mining task primitives.
These primitives allow the user to interactively communicate with the data mining system during discovery in order to direct the mining process, or examine the findings from different angles or depths. The data mining primitives specify the following, as illustrated in Figure 1.13.
The set of task-relevant data to be mined: This specifies the portions of the database or the set of data in which the user is interested. This includes the database attributes or data warehouse dimensions of interest (referred to as the relevant attributes or dimensions).
The kind of knowledge to be mined: This specifies the data mining functions to be performed, such as characterization, discrimination, association or correlation analysis, classification, prediction, clustering, outlier analysis, or evolution analysis.
The background knowledge to be used in the discovery process: This knowledge about the domain to be mined is useful for guiding the knowledge discovery process and for evaluating the patterns found. Concept hierarchies are a popular form of background knowledge, which allow data to be mined at multiple levels of abstraction. An example of a concept hierarchy for the attribute (or dimension) age is shown in Figure 1.14. User beliefs regarding relationships in the data are another form of background knowledge.
The interestingness measures and thresholds for pattern evaluation: They may be used to guide the mining process or, after discovery, to evaluate the discovered patterns. Different kinds of knowledge may have different interestingness measures. For example, interestingness measures for association rules include support and confidence. Rules whose support and confidence values are below user-specified thresholds are considered uninteresting.
The expected representation for visualizing the discovered patterns: This refers to the forminwhich discovered patterns are to be displayed,which may include rules, tables, charts, graphs, decision trees, and cubes.
A data mining query language can be designed to incorporate these primitives, allowing users to flexibly interact with data mining systems. Having a data mining query language provides a foundation on which user-friendly graphical interfaces can be built.
This facilitates a data mining system’s communication with other information systems and its integration with the overall information processing environment.
Designing a comprehensive data mining language is challenging because data mining covers a wide spectrum of tasks, from data characterization to evolution analysis. Each task has different requirements. The design of an effective data mining query language requires a deep understanding of the power, limitation, and underlying mechanisms of the various kinds of data mining tasks.
There are several proposals on data mining languages and standards. In this book, we use a data mining query language known as DMQL (Data Mining Query Language), which was designed as a teaching tool, based on the above primitives. Examples of its use to specify data mining queries appear throughout this book. The language adopts an SQL-like syntax, so that it can easily be integrated with the relational query language, SQL. Let’s look at how it can be used to specify a data mining task.
Chapter: Data Warehousing and Data Mining
Data mining primitives.
Data mining primitives.
A data mining query is defined in terms of the following primitives
Task-relevant data: This is the database portion to be investigated. For example, suppose that you are a manager of All Electronics in charge of sales in the United States and Canada. In particular, you would like to study the buying trends of customers in Canada. Rather than mining on the entire database. These are referred to as relevant attributes
The kinds of knowledge to be mined: This specifies the data mining functions to be performed, such as characterization, discrimination, association, classification, clustering, or evolution analysis. For instance, if studying the buying habits of customers in Canada, you may choose to mine associations between customer profiles and the items that these customers like to buy
Background knowledge: Users can specify background knowledge, or knowledge about the domain to be mined. This knowledge is useful for guiding the knowledge discovery process, and for evaluating the patterns found. There are several kinds of background knowledge.
Interestingness measures: These functions are used to separate uninteresting patterns from knowledge. They may be used to guide the mining process, or after discovery, to evaluate the discovered patterns. Different kinds of knowledge may have different interestingness measures.
Presentation and visualization of discovered patterns: This refers to the form in which discovered patterns are to be displayed. Users can choose from different forms for knowledge presentation, such as rules, tables, charts, graphs, decision trees, and cubes.
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Courses 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:
Data mining is the process of extracting knowledge or insights from large amounts of data using various statistical and computational techniques. The data can be structured, semi-structured or unstructured, and can be stored in various forms such as databases, data warehouses, and data lakes.
List of Data Mining Task Primitives A data mining query is defined in terms of the following primitives, such as: 1. The set of task-relevant data to be mined This specifies the portions of the database or the set of data in which the user is interested.
Data mining refers to extracting or mining knowledge from large amounts of data. In other words, Data mining is the science, art, and technology of discovering large and complex bodies of data in order to discover useful patterns.
Overview of Data Mining Task Primitives Data is the most important part of an industry, and it's important to understand it. Understanding data simply means finding its characteristics, patterns, and trends. To do all these operations, Data mining provides us with methods and functions.
The four parameters of data mining: The first parameter is to fetch the relevant dataset from the database in the form of a relational query. By specifying this primitive, relevant data are retrieved. The second parameter is the type of resource/information extracted.
Discuss Courses Data preprocessing is an important step in the data mining process. It refers to the cleaning, transforming, and integrating of data in order to make it ready for analysis. The goal of data preprocessing is to improve the quality of the data and to make it more suitable for the specific data mining task.
Here is the list of descriptive functions − Class/Concept Description Mining of Frequent Patterns Mining of Associations Mining of Correlations Mining of Clusters Class/Concept Description Class/Concept refers to the data to be associated with the classes or concepts.
Technically, data mining is the computational process of analyzing data from different perspectives, dimensions, angles and categorizing/summarizing it into meaningful information.
Purpose: Descriptive data mining is used to describe the data and identify patterns and relationships. Predictive data mining is used to make predictions about future events. Approach: Descriptive data mining involves analyzing historical data to identify patterns and relationships.
We can specify a data mining task in the form of a data mining query. This query is input to the system. A data mining query is defined in terms of data mining task primitives. Note − These primitives allow us to communicate in an interactive manner with the data mining system. Here is the list of Data Mining Task Primitives −. Set of task ...
Data Mining Primitives 1. Task-relevant data: What is the data set I want to mine? 2. Type of knowledge to be mined: What kind of knowledge do I want to mine? 3. Background knowledge: What background knowledge could be useful here? 4. Pattern interestingness measurements: What measures can be useful to estimate pattern interestingness? 5.
Data mining tasks are designed to be semi-automatic or fully automatic and on large data sets to uncover patterns such as groups or clusters, unusual or over the top data called anomaly detection and dependencies such as association and sequential pattern.
Data mining functionalities are used to represent the type of patterns that have to be discovered in data mining tasks. In general, data mining tasks can be classified into two types including descriptive and predictive. Descriptive mining tasks define the common features of the data in the database and the predictive mining tasks act inference ...
Data mining primitives: What defines a data mining task? Why Data Mining Primitives and L anguages ? Finding all the patterns autonomously in a database? — unrealistic because the patterns could be too many but uninteresting Data mining should be an interactive process
Data Mining is defined as the procedure of extracting information from huge sets of data. In other words, we can say that data mining is mining knowledge from data.
4. Association Rules: This data mining technique helps to discover a link between two or more items. It finds a hidden pattern in the data set. Association rules are if-then statements that support to show the probability of interactions between data items within large data sets in different types of databases.
Descriptive Classification and Prediction Descriptive Function The descriptive function deals with the general properties of data in the database. Here is the list of descriptive functions − Class/Concept Description Mining of Frequent Patterns Mining of Associations Mining of Correlations Mining of Clusters Class/Concept Description
A data mining query is defined in terms of data mining task primitives. These primitives allow the user to interactively communicate with the data mining system during discovery in order to direct the mining process, or examine the findings from different angles or depths.
Data mining primitives. A data mining query is defined in terms of the following primitives Task-relevant data: This is the database portion to be investigated. For example, suppose that you are a manager of All Electronics in charge of sales in the United States and Canada.
A Computer Science portal for geeks. It contains well writing, well thought and fine explained computer science and programming articles, quizzes and practice/competitive programming/company interview Questions.
Data mining task is some form of data analysis that a person likes to perform on dataset. data mining task is often specified in the form of a data mining query, that is input to the data mining system. A data mining query is outlined in terms of data mining task primitives.
Data mining is the act of automatically searching for large stores of information to find trends and patterns that go beyond simple analysis procedures. Data mining utilizes complex mathematical algorithms for data segments and evaluates the probability of future events. Data Mining is also called Knowledge Discovery of Data (KDD).