5 Data Mining Techniques Businesses Need To Know About
With information flowing in from a number of sources — websites, mobiles, social media, and other digital channels, organizations are swarmed with volumes of data today. But the question that continues to remain unanswered, is how businesses can make use of this data. The answer lies in data mining.
Let’s take a look at the five data mining techniques that can help businesses garner actionable insights from all the data.
1) Classification analysis: Data is classified into different sets in order to reach an accurate analysis or prediction. An example of application of classification analysis is when banks try to determine who should be offered a loan.
Applying classification analysis to the database, they can define the predictors — annual income, age etc. and the predictor attributes — numerical values corresponding to the predictors. Using IF/THEN analysis, they can then decide whether someone qualifies for the loan. For example, if the age is more than 20 years and income is equal to or more than Rs50000 per month, they qualify for the loan.
2) Association Rule Learning: By far the largest application of association rule learning has been in forecasting customer behavior. This is because the technique helps identify relationships between different variables and establish hidden patterns in the data. This data mining technique is widely used for analyzing sales transactions.
An example of association rule learning in an industry like online retail could be — A user who buys product ‘A’ and also product ‘B’, is likely to buy product ‘C’ for a consequent need.
3) Anomaly or Outlier Detection: This techniques digs into the outliers in a data set. Outliers/anomalies are patterns that do not match expected behavior. When an event that does not conform to a predefined pattern occurs, data analysts categorize this as noise and remove it from the remaining data set. Also, when outliers are detected analysts try to find out what caused the disturbance in the expected patterns. System health monitoring and fault detection are two applications of outlier detection.
4) Clustering Analysis: In this technique, data objects are grouped in clusters on the basis of similarity. The idea is to group data objects in such a manner that the degree of association is maximal within each cluster and minimal outside it. For instance, clusters of symptoms such as paranoia, schizophrenia etc. need to be correctly diagnosed in psychiatry, for the right therapy to be started.
5) Regression Analysis: In this type of analysis technique, there is a response variable and one or more than one predictor. The predictor variable(s) are independent and the responsive is dependent. The technique is used for studying how changing the value of predictor can alter the value of responsive variables. Note that only altering the predictor values can change the values for responsive, and this is not true vice-versa. Regression analysis is being used since long as a forecasting technique, and to study causal relationships.
In businesses, regression analysis can be used to predict events yet to occur. Insurance companies, for example, use regression analysis to find out how many people will be victims of theft.
Optimizing business processes is another application of regression analysis. For example, a company might want to understand and optimize the wait time of a customer call and the number of successful sales, to find out what should be the optimum wait time for a client call to be answered.
Each of the five discussed data mining techniques can help businesses gain valuable insights from data and use it solve tough business problems. Converting raw data into knowledge is the key to making better, smarter, and informed decisions.
