- Machine Learning Tutorial
- Data Analysis Tutorial
- Python – Data visualization tutorial
- Machine Learning Projects
- Machine Learning Interview Questions
- Machine Learning Mathematics
- Deep Learning Tutorial
- Deep Learning Project
- Deep Learning Interview Questions
- Computer Vision Tutorial
- Computer Vision Projects
- NLP Project
- NLP Interview Questions
- Statistics with Python
- 100 Days of Machine Learning
- Explore Our Geeks Community
- Pathfinder Optimization Algorithm
- ML | Introduction to Kernel PCA
- Factorized Random Synthesizer
- Hyperparameters of Random Forest Classifier
- Instance-based learning
- Differentiate between Support Vector Machine and Logistic Regression
- Improving Business Decision-Making using Time Series
- Locally weighted linear Regression using Python
- Henry gas solubility optimization
- Artificial Intelligence: Cause Of Unemployment
- AHA: Artificial Hippocampal Algorithm
- Overview of SIR Epidemic Model for Corona Virus Outbreak Prediction
- Facebook’s TransCoder AI Converts Code Between Multiple Programming Languages
- Teaching Learning based Optimization (TLBO)
- Short term Memory
- Neural Logic Reinforcement Learning - An Introduction
- Underflow and Overflow with Numerical Computation
- Local Relational Network
Difference Between Descriptive and Predictive Data 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:
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
Please Login to comment...
- Machine Learning
Please write us at contrib[email protected] to report any issue with the above content
Improve your Coding Skills with Practice
Data Mining 365
- Data Mining
- _Data Mining
- Data Warehousing
Data Mining Primitives Explained In Detail
Types Of Data Used In Cluster Analysis - Data Mining
Classifier Accuracy Measures In Data Mining
Partitional Clustering - K-Means & K-Medoids
Menu Footer Widget
Spreading knowledge and educating digitally
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.
Copyright © 2018-2024 BrainKart.com; All Rights Reserved. Developed by Therithal info, Chennai.
404 Not found
- Interview Q
- Send your Feedback to [email protected]
Help Others, Please Share
Learn Latest Tutorials
Python Design Patterns
B.Tech / MCA
JavaTpoint offers too many high quality services. Mail us on h [email protected] , to get more information about given services.
- Website Designing
- Website Development
- Java Development
- PHP Development
- Graphic Designing
- Digital Marketing
- On Page and Off Page SEO
- Content Development
- Corporate Training
- Classroom and Online Training
Training For College Campus
JavaTpoint offers college campus training on Core Java, Advance Java, .Net, Android, Hadoop, PHP, Web Technology and Python. Please mail your requirement at [email protected] . Duration: 1 week to 2 week