What are different stages of Data mining

What are different stages of Data mining

What are different stages of Data mining

Exploration: This stage start with preparing data such as data cleaning, transformation, selecting and records. Based on sized of data, different tools to analyze the data may be required. This stage helps to determine different variable of the data to determine their behavior.

ModelBuildingand Validation: In this stage involves choosing the best model based on their predictive performance. This sounds like an easy task but can be difficult. This stage is also called as pattern identification.

Deployment: Until the consistent pattern is found in stage in stage 2, can be applied for the purpose to see whether the desired outcome achieved.

What are different stages of Data mining

What are different stages of Data mining

There are certain stages to data mining that you will want to become familiar with, and these are exploration, pattern identification, and deployment.

1. EXPLORATION:

Exploration is the first stage, and as the name implies, you will want to explore and prepare data. You may need to clean the data you have, or it may need to be transformed into another form.

2. PATTERN IDENTIFICATION:

After you've explored, refined, and defined specific variables, you will next want to move on to stage 2, which is also called pattern identification.

3. DEPLOYMENT:

The third stage is called deployment. You will not want to move to this stage until you have found a consistent pattern from stage 2 that is highly predictive.

What are different stages of Data mining

There are three stages of the Data Mining Process. The first stage of Data Mining Process is Exploration and has a goal of to find important variables and determine their nature. You might have to clean the data you have so that it could be transformed into another form. Also create some records and have a large number of variables to consider.

You may also need to reduce a range that is easy to deal with based on what kind of problem that you are trying to solve. The wider selection of tools in the order to analyze your data. The second stage is Pattern Identification. You will look for patterns and choose one that will allow you to make the best predictions.

There will be a wide variety of different ways you can find the best predictive patterns. The patterns will allow your store to earn more profits. Last the third stage is Deployment. You will have found a consistent pattern from the second stage.

Many of your customers are consistently buying a specific product on a certain date. Data mining is very popular term for many companies and organizations.

What are different stages of Data mining

The data mining and cluster relationships.

Most marketers understand the value of collecting Cluster data, but also realize the challenges of leveraging this knowledge to create intelligent, proactive pathways back to the customer. Data mining – technologies and techniques for recognizing and tracking patterns within data –

Helps, business sift though layers of seemingly unrelated data for meaningful relationships, where they can anticipate, rather than simply react to, customer needs.

I hope you can get idea.

Thank you and more Power.

Regards;

henry

What are different stages of Data mining

What are different stages of Data mining

To begin with Data mining can be described as a logical process that is used to search through bulk amounts of information in order to find important data. The aim of this technique is to find patterns that were previously unknown. Behavior patterns gotten from data mining can be used to solve a number of problems.

Once have gotten a hand of a data mining behavior pattern, you will be able to make strategic decisions that can allow you to achieve certain goals. In other to achieve data mining you will have to be familiar with the 3 stages of data mining which are: exploration, pattern identification, and deployment. Read for detailed explanation of stages data mining.

Stages of Data Mining:

Exploration Stage:

This is the first stage and as the name implies, you will want to explore and prepare data. Here the data gotten may need to be cleaned or transformed into another form. Also in addition to this you may need to create records. If a large number of variables where gathered during your exploration you may need to reduce them to a range that is easy to deal with.

You may need to use a wider selection of tools in order to analyze the data. Example of tools you could use are graphs and statistics. The exploration stage is all about finding the important variables and determining their nature.

Pattern Identification Stage:

After exploration is the second stage which is known as pattern identification. At this stage you will look for look for patterns and choose the ones that will allow you to make the best predictions. This stage of data mining can be somewhat complex. There are a variety of ways you can use to find the best predictive patterns.

One of the best ways is to apply different patterns to a given situation and determine which one performs at the highest level. For example, if you are looking at data to find patterns that will allow your store to increase your profits, you could take two shopping patterns of your clients and apply them to a hypothetical strategy to determine which one would result to the best.

Deployment Stage:

The third of the data mining stages which is the last stage is called deployment. This stage comes after you must have found a consistent pattern that is highly predictive from stage 2. For example, if you find more customers purchasing a specific product on a certain date, you will be able to predict their future behavior pattern. Now that you've done this, you can take the pattern and apply it in order to see if you can achieve your desired outcome.

I hope my explanation of the stages was helpful to you.

What are different stages of Data mining

There are three stages of the Data Mining Process. The first stage of Data Mining Process is Exploration and has a goal of to find important variables and determine their nature. You may need to clean the data you have, or it may need to be transformed into another form.

After explored, refined, and defined specific variables, next stage is called pattern identification.

Until the consistent pattern is found in earlier stage deployment can be applied for the purpose to see whether the desired outcome achieved.