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Types of Data Mining Techniques

Data mining was developed on the ground that business owners found the necessity of data (crucial facts and relevant information) without having to depend on statisticians. Data mining is a frequently taken action. Nature of data mining is analytical and it helps explore bulk business-related data to identify, validate and predict general trends. Building effective business models greatly depends on data mining operations. Given here are a few important data mining techniques that include data classification, prediction, association, clustering and sequential patterns.

Important Elements of Data Mining

 

1. Storing and Managing data in a multi-level database setup.
2. Presenting accumulated data in a handy format such as through table or a graph.
3. Data warehouse is the place where all data is extracted, transformed and loaded.
4. Application software analyses the data collected.
5. Details of all data access are provided to business analysts and IT industry professionals.

 

Techniques of Data Mining

There are some major techniques involved in bringing out the best of data mining which include prediction, preparation, association, clustering, Deployment, decision tree, classification, Drill-Down Analysis and sequential patterns. We have provided a brief overview of each technique for better understanding.

Prediction

Predictive data mining is applied to identify a set of models such as a statistical or neural network model. This technique is used to predict response of interest. This technique is different from all other mining techniques as this is not explorative in nature but works on probability models and variable factors. Predictions are made on the basis of an assessment of relation between dependent and independent variables. These analysis help sales departments to predict future profit. However sale can be regarded as an independent or a dependent variable. Related results can be perceived on the basis of historical data on profit and sales.

Boosting is a concept that is applied in predictive form of data mining in order to bring out multiple models, derive weights (required in combining predictions) and generate classifiers. Boosting is done by starting a method and learning the data structure acquired by it. A series and sequence of classifiers is derived to predict the classifications. This is known as a boosting process.

Preparation

Preparing data classes and structures is a very important technique of data mining procedure. The bulk process of selecting and rejecting applicable and inapplicable data respectively serves as the basis of analysis. Acquired Data can contain bulk (and often impossible combinations) and out of rage values. Uncontrolled data acquiring leads to massive errors. To avoid errors, preparation of well-accumulated data is done to achieve crispness or clean data in data mining.

Association

This is a well-known data mining technique that forms patterns from relationships between items in similar transactions. Association technique is also popularly known as relation technique. This technique is applied to identify a range of products frequently purchased by consumers.

Retailing giants tend to use this technique in researching customers’ buying habits. For example by assessing historical data a retailer may find a trend where customers tend to buy chips along with beer. Going by this trend the retailer can choose to place beer and chips in the same place to save time for consumers and pull up sales figures.

Clustering

Meaningful clusters of objects are created with this technique. Objects having similar characteristics are defined into classes. The setting of classes and placing objects into each of them defines the clustering technique. We can take the example of categorizing books (book management in a store or a library) according to their genres in a library. Readers find it easy to scan and buy books as each shelf is separated with meaningful labels.

Deployment

Predictive data mining utilizes the process of deployment. Deployment technique includes putting up a model for prediction or classifying them into a new data. After a model is chosen or a set of models has been identified there is a need to deploy these models. After deployment the aim of achieving predicted classifications for further obtaining of new data is completed. This technique is often used in tracking types of transaction done through credit cards.

Decision tree

A predictive model is generally viewed as a decision tree. Taking an example of the mobile phone industry the tree includes customer classifications. Each branch of a decision tree includes classification questions. Data is divided into various parts in the tree. For data related to mobile phone industry the data point include number of churners and non-churners. This gives a brief about how the data model is built. Marketing departments find it easy to trace the flow of customers and target them accordingly. An intuitive path on customer base is drawn with the help of this technique.

Classification

Data mining involves setting up classes and groups in order to classify data into these predefined groups. Classification technique is a different form of data mining that involves mathematical representations such as neural network, decision trees, statistical data and linear programming. A software is developed in this process that does all the task of classifying data items into groups. This technique is best suited for keeping up employee records by classifying them into separate groups. Project wise upkeep of employee records can help organization to figure out which employee would remain available or unavailable for ongoing and future projects.

Drill-Down Analysis

Large databases require drill-down analysis of data mining. Explored data are broken down on the basis of interest-related variables like geographic region, age, gender, etc. Each group is aligned with histograms, statistics, graphical overviews and tables. Drill down technique is made to expose (analyze in details) these categorizations. We can take the example of service providers who may think of reviewing addresses of particular gender of customers according to their income groups. This is utilized in offering them future services according their group classification.

Sequential Patterns

Discovering regular events, trends and identifying similar patterns in transaction data is the basis of ‘Sequential Patterns’ technique. Customer acquisition is greatly boosted with the help of this technique. Businesses can track customer buying trends and offer better deals by using significant sales data.

Clubbing one or two techniques together and forming an effective business process is the sole intent of data mining. There are related testing and computational designs that go on to make successful data mining processes.