KDD vs Data Mining.
Hello Friends, In this blog post(KDD vs Data Mining) we are going to discuss the difference between KDD and Data mining.
The full form of KDD is knowledge discovery in the database.|KDD vs Data Mining|
Knowledge discovery in database (KDD) was formalized in 1989.|KDD vs Data Mining|
Concerning the general concept of being broad and high level in the pursuit of seeking knowledge from data.|KDD vs Data Mining|
The term data mining was then coined; this high-level application approach is employed to present and analyze data for decision-makers.|KDD vs Data Mining|
Data mining is among such important steps involved in the knowledge discovery process that encompasses data selection, data cleaning, and preprocessing,…
… data transformation and reduction, data algorithm choice, and finally post-processing and the interpretation of the discovered knowledge.
The KDD process tends to be highly iterative and interactive.
Data mining analysis tends to work from the data and the best techniques are developed with an orientation toward large volumes of data…
…, making use of as much data as possible to arrive at reliable conclusions and decisions.
The analysis process starts with a set of data and uses a methodology to develop…
…an optimal representation of the structure of data, during which knowledge is acquired.
Once knowledge is acquired, this can extend to large sets of data on the…
… the assumption that the large data set has a structure similar to the simple data set.
Fayyad et al distinguish between KDD and data mining by giving the following definition.
knowledge discovery in the database in the process of identifying a valid potentially…
…useful and ultimately understandable structure in data.
This process involves selecting or sampling data from a data warehouse, and selecting or preprocessing it,…
… transforming or reducing it, applying a data mining component to produce a structure, and then evaluating the derived structure.
Data mining is a step in the KDD process related to the algorithmic means by which patterns or…
…structures are counted from the data under acceptable computational efficiency limitations.
Thus, the formats that are the outcome of the data mining process…
…must meet certain conditions so that these can be considered knowledge.
These conditions are validity understandability, utility, novelty, and interestingness.
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Quick Q&A:
Is data mining also known as KDD?
Yes, Data mining is also referred to as KDD(knowledge discovery from data).
This is an automated or convenient process of extracting knowledge patterns.
And these patterns are implicitly stored or kept in the large database,…
…the web, data warehouses, and other massive information repositories.
What are the 4 stages of data mining?
The four stages of data mining are given below:
(1) data acquisition
(2) data cleaning, preparation, and transformation
(3) data analysis, modeling, classification, and forecasting
(4) reports.
What are the data mining techniques?
The data mining techniques are given below:
Classification.
Clustering.
Association Rule Learning.
Regression.
Anomaly Detection.
Sequential Pattern Mining.
Artificial Neural Network Classifier.
Outlier Analysis.
What are 3 data mining techniques?
See, there are various data mining techniques.
But a few important or prevalent ones are given below:
clustering,
data cleaning,
association,
data warehousing,
machine learning,
data visualization,
classification,
neural networks,
prediction.
What are the 6 processes of data mining?
The six processes of data mining are given below:
business understanding,
data understanding,
data preparation,
modeling,
evaluation,
deployment.
What is OLAP in data mining?
OLAP stands for online analytical processing.
This software is used to perform multidimensional analysis at high speed on a large set of data.
This large set of data could be from the data warehouse, data mart, or other unified and centralized data stores.
What is the difference between OLAP and OLTP?
The OLAP and OLTP are two different data processing systems.
And they both are designed to serve different purposes.
On the one hand, OLAP is used for complex data analysis and reporting.
On the other hand, OLTP is used for transactional processing and real-time updates.
What is ETL in data mining?
ETL stands for extract, transform, and load.
Using this process data from multiple sources are combined into a large, central repository called a data warehouse.
A few sets of business rules are used by ETL to clean and organize raw data.
Thus this process prepares the data for storage, data analytics, and machine learning.
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