data mining techniques

© 2020 - EDUCBA. Often, we use decision trees to help build and identify classifications that we can track for a longer period to identify sequences and patterns. This is …

As new information, events, and data points are identified, it might be necessary to build more branches, or even entirely new trees, to cope with the additional information. Every neural network model has different architectures and these architectures use different learning procedures. They are as follows. In this technique, each branch of the tree is viewed as a classification question and the leaves of the trees are considered as partitions of the dataset related to that particular classification.

You must work with your data, reformat it, or restructure it, regardless of whether you are using SQL, document-based databases such as Hadoop, or simple flat files. There are a lot of ways to collect data like. Using the credit card authorization, for example, you might combine decision tree analysis of individual past transactions with classification and historical pattern matches to identify whether a transaction is fraudulent. It is recent that the very large data sets and the cluster and large-scale data processing are able to allow data mining to collate and report on groups and correlations of data that are more complicated.

Let’s look at some key techniques and examples of how to use different tools to build the data mining. This technique is closely related to the cluster analysis technique and it uses the decision tree or neural network system.

Additionally, you can use classification as a feeder to, or the result of, other techniques. In practice, it’s very rare that you would use one of these exclusively.

By analyzing past events or instances, you can make a prediction about an event. As long as you apply the correct logic, and ask the right questions, you can walk away with conclusions that have the potential to revolutionize your enterprise.

For example, an insurance company can group its customers based on their income, age, nature of policy and type of claims.

With 30 or 40 million records of detailed customer information, knowing that two million of them live in one location is not enough. You also need to be able to identify anomalies, or outliers in your data. For example, if you’re evaluating data on individual customers’ financial backgrounds and purchase histories, you might be able to classify them as “low,” “medium,” or “high” credit risks.

Association is related to tracking patterns, but is more specific to dependently linked variables. For example, within InfoSphere Warehouse a wizard provides configurations of an information flow that is used in association by examining your database input source, decision basis, and output information.

For example, you can easily classify cars into different types (sedan, 4×4, convertible) by identifying different attributes (number of seats, car shape, driven wheels).

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