data mining techniques syntelli solutions inc

Development of information technology has generated a ton of data and databases across diverse areas. The extraction of useful information and patterns from this data is carried out through a process called data mining or knowledge discovery. Data mining has become vital across many business applications for effective decision-making. It is defined as a process used to find models, interesting trends, and patterns in large data sets to make decisions about future activities and requires tools to explain data and make predictions. It integrates techniques from several areas such as machine learning, artificial intelligence, pattern recognition, statistics, and database systems for the analysis of large volumes of data.

Popular Data Mining Techniques:

Data mining techniques are used for data analysis and knowledge discovery from a database that helps in finding patterns to discover future trends in businesses and also to make predictions using data.

Classification – It is a highly used data mining technique that uses a set of pre-defined examples to develop a model to classify the data at large. This data mining technique is greatly used for fraud detection and credit risk application. This process involves learning and classification. Learning analyzes data with a classification algorithm and classification uses test data to determine the accuracy of classification rules.

Clustering – This technique finds similar classes of objects, therefore businesses use this technique to find dense and sparse regions in object space and discover overall distribution patterns and correlations among data attributes. Businesses use this technique to form a group of customers based on their purchasing patterns and to categorize genes with similar functions.

Regression – This technique is used to model the relationship between one or more independent and dependent variables. This technique is adopted by businesses to make predictions. In data mining, independent variables are already known, and response variables are predicted. For example, this technique can be used to predict future profit considering sale as an independent variable and profit as the dependent variable. Based on the prior sale and profit data, businesses can draw a fitted regression curve to predict future profit.

Association – Association rule uses machine learning models to analyze data for patterns or co-occurrence within large data sets in various types of databases. Association rule comprises of two parts, an antecedent (if) and a consequent (then). This is one of best-known data mining techniques in which a pattern is discovered based on a relationship of a specific item on other items in the same transaction. This rule is generally applied on large amount of data, for example it is used in market basket analysis to identify products that customers usually purchase together.

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