- Data mining is a process that discovers the patents that involve different methods which meet at the nooks of database systems and machine learning.
- Data mining is the step involved in the analysis of the discovered knowledge where database systems are concerned.
- The main objective of data mining is to extract the patterns and not the data.
- It happens in an automatic manner (or semi-automatic). Some of its types are cluster analysis, the non0usual records, sequential patterns, association rule mining, etc.
- Data Dredging, snooping, fishing, are all the methods that may be used in order to fetch the patterns of the data mined.
Oh! So, thanks! Those were the 5 things you need to know about Data Mining. Thanks for being such a patient reader.
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KDD Process
KDD refers to the Knowledge Discover of Database. It generally has the following processes involved.
- Selection
- Pre-processing
- Transformation
- Data Mining
- Evaluation
There are many variations that could further define these processes which can be very well confined by the following.
The Six Phases (CRISP-DM)
- Business Understanding
- Data Understanding
- Data Preparation
- Modelling
- Evaluation
- Deployment
So much information in just 200 words! Are you feeling lucky already?
Data mining is a misnomer. The reason behind that is that the extraction of knowledge and patterns derived from a huge measure of data and is used to be applied for a large-scare data. The actual task the automatic or semi-automatic analysis of quantities of data which is, in turn, used to pull out previously no-known, intriguing patterns which is also known as CLUSTER ANALYSIS. By using spatial indices, this can be taken to another level.
Classes of Data Mining
There is a total of 6 classes belonging to data mining.
Association Rule Learning
This is also known as dependency modelling. It refers to the searches that occur between different variables. For instance, customer purchasing behaviour might be the basis for a supermarket to collect data. At the same time, using association rule, (like Amazon) the “frequently bought together” could be materialised. The information regarding the products
that are more than often go home in the same cart can then be used for marketing and remarketing purposes. Heard of “MARKET BASKET ANALYSIS”? This is it.
Anomaly Detection
Anomaly detection is known and referred to by a number of methods; outlier, change, and deviation detection. It is the detection and identification of unusual data records. That can be collected for further investigation.
Clustering
This is the process of identifying new structures and groups which are exactly the same of similar. This is done without the knowledge of those data structures which are already known.
Classification
This process generalises the structure as a whole in order to use the new data obtained. This is evident from the email you use. The mechanism that works behind involves the classification of a new email as spam or a legitimate one.
Regression
This is the classification that tries and pin-points that structure which models and updates the data with the least number of errors. This is done for a mannerly estimation of data representation in a decent measure of a set of a data.
Summarising
This phase simply provides a better, concise, precise, and compact representation of a data set which uses tools including the likes of generation of a report and visualisation.
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