Data Mining Techniques Best Result With Applications

Data Mining Techniques Best Result With Applications
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Data Mining Techniques are developed using various methods which include classification, clustering; prediction is such methods which are used in this. Before moving further, we must know the meaning of the word “Data Mining.”





DEFINITION OF DATA MINING TECHNIQUES:-
Data mining Techniques is a method of discovering patterns which are found in large data sets, which involves methods at intersection of statistics, machine learning and database systems. Data Mining is an overall extraction of information which is stored in data sets. Data Mining Techniques is an analysis step of “knowledge discovery in database” process.

Data Mining Techniques can be the misnomer, the reason behind this is that its primary goal is the extraction of information from specific sets of patterns of data it contains.

Data mining Techniques can use we used during the construction of Warehouse, business intelligence, decision support system, artificial intelligence.

When we are discussing data mining techniques that we must also know about one of the most critical process, i.e., KDD PROCESS.”

data mining

KNOWLEDGE DISCOVERY IN DATABASE(KDD):-
KDD process mainly consists of four main stages, which are summed up below:-

  1. Selection
  2. Pre-processing
  3. Transformation
  4. Data mining
  5. Evaluation

Now we will discuss above points one by one.
PREPROCESSING:-
This method is applied to target set data which are assembled. Pre-processing can be essential to analysis the multivariate present in the data set which is a target before data mining Techniques. Data mining Techniques is done after this method is performed. It is also done so that data is cleaned, free from noise and information which we get should be in incomplete.

 

DATA MINING:-
Data mining Techniques is a process of identification of unusual data records; data error are removed, and then we extract information from the dataset. Well, it also has main components such as classification, summarization, regression, etc.

SELECTION:-
When we see any data, then we try out to find out any patterns it contains. After finding a model present in the data, we select that data. After selection the data it sends to another process which is termed as transforming.

TRANSFORMING:-
Transforming of data means to summarized data in order so that we must get information for next process.

EVALUATION:-
It means to analyze data contains in a data set. Analysis of data gives us the exact face of information it wants to convey with us or what it has stored in it. This helps us in the construction of “DATA WAREHOUSE.”

data mining techniques

DATA MINING TECHNIQUES:-
Now we will see that what is the Data Mining Techniques used:-

ASSOCIATION OF DATA MINING TECHNIQUES:-

In this process, a specific pattern is discovered based on the relationship between the items in which transaction has to be made. It is used in market basket analysis to identify a product which is more in demand. Retailers are using this technique to find out the buying habit of the customer.
As retailers may find that when customer buy beers, they also buy chips which can be seen as a habit.




CLASSIFICATION OF DATA MINING TECHNIQUES:- 

It is based on machine learning techniques. The classification used to classify each item present in the dataset. Classification is done while grouping similar data into one group. Each group of data must differ from another group or set of data.
Classification uses mathematical technique such as neural technique, linear programming and statistics.

CLUSTERING OF DATA MINING TECHNIQUES:- 

Clustering makes useful information of similar characteristics are grouped into one cluster. Clustering is used to define a type of data it has. To understand this, we can mention an example of a library management system. When we go to the library, we find books in a secure manner which we want to read where so many books are present. The technique behind this is Clustering because the books which are of the same type are kept on one shelf so that readers can easily find it.

DECISION TREES OF DATA MINING TECHNIQUES:-
The decision tree is the most common type of data mining techniques.
Decision tree starts with a root where specific questions are put up, or specific conditions are given. Then after that questions or conditions are applied, then we get different sets of answers from it. With different sets of solutions, we multiply it with different conclusions that arose from it. After that, we come to one end that is termed as the conclusion. This is how the decision is made.

SEQUENTIAL PATTERNS OF DATA MINING TECHNIQUES:-
The sequential pattern helps us to analyze the patterns of similar type, regular events or trends in transfer data.
In business, historical transfer data are used to find out a set of items which are more preferred by customers and which things are in more demand in markets. On this basis, they can deal with their profits or loss of their product’s price in the future.

BENEFITS OF DATA MINING TECHNIQUES TECHNIQUES:-
The strengths of data mining can be seen in the following fields:-

DATA MINING TECHNIQUES IN MARKETING/RETAIL:-
Data Mining helps a lot in the market sector as by this market companies can find out customers demand on historical data sets on which they prepare the schemes of their product for its more demand. It also helps these retailers to find out the new customers so that they can target them for selling their new product between them.

DATA MINING TECHNIQUES IN FINANCE/BANKING:-
Data mining helps a lot in the finance and banking sector. As we see a historical data set on the massive scale of credits on loans, there were given to customers, with this financial and banking will come to know whether their schemes are good or bad.

On that basis, they can decide on further framing their future schemes.

data mining techniques

APPLICATIONS ON DATA MINING TECHNIQUES:-

There are some applications of data mining techniques are as follows:-

  1. Future healthcare.
  2. Market Basket Analysis.
  3. Manufacturing engineering.
  4. Fraud Detection.
  5. Intrusion Detection.
  6. Customer Segmentation
  7. Financial Banking.




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