While large-scale information technology has developed separate transaction and systems analysis, data mining provides the connection between the two. Data mining software analyzes the relationships and patterns of stored transaction data is based on open-ended questions. Different types analytical software applications available: statistics, machine learning, and neural networks.
In general, any of the Four Type Relations is designed to:
- Classes: saved data is used to locate data in predefined groups.
For example, a restaurant chain could mine customer purchase data to determine when customers visit and what they typically order. This information could be used to increase traffic by having daily specials.
- Clusters: Data items are grouped according to logical relationships or consumer preferences. For example, data can be mined to identify market segments or consumer affinities.
• Associations: The data can be mined for the identification of compounds. The beer- nappy example is an example of one of the clubs of mines.
• Sequential Patterns: The data is the minefields which provide behaviour patterns and trends. For example, an outdoor equipment retailer could provide for the possibility a backpack that is purchased on the basis of the consumer to purchase sleeping bags and hiking shoes.
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Data Mining is composed of Five Key Areas
- Extract, transform, and load transaction data in data warehouse system.
- Store and manage the data in a multidimensional database system.
- Provide data access to business analysts and information technology professionals.
- Analyze the data by application software.
- Present the data in a useful format, such as a graph or table.
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Different Levels of Analysis are available:
· Artificial Neural Networks: Non-linear predictive models that are taught through training and resemble biological neural networks in the structure.
· Genetic Algorithms: Optimization techniques using methods such as genetic combination, mutation and natural selection in a design based on the concepts of natural development.
· Decision Trees: Trees shaped structures representing sets of decisions. These decisions will produce rules for the classification of the data.
· Nearest Neighbor Method: A technique that classifies each record in a data set is based on a combination of categories of k(s) more similar to a historical dataset sometimes called k- nearest neighbor technique.
· Rule induction: Mining useful if-then rules from data based on statistical significance.
· Imaging Data: The visual interpretation of complex relationships in multi-dimensional data. Graphics tools are used to visualize data relationships.
· Rule induction: Mining useful if-then rules from data based on statistical significance.
· Imaging Data: The visual interpretation of complex relationships in multi-dimensional data. Graphics tools are used to visualize data relationships.
We discussed benefits and advantages of data mining, but what is necessary with Advantage to create good Strategy of Data Mining?
1. Business Understanding: Objectives and problems of business are certain and transformed to a problem of data mining. The initial plan is ready.
2. Understanding of Data: Data are taken away originally. The information concerning structure, qualities and subsets of data is found out.
3. Preparation of Data: The Final data set is constructed. After sorting and preparation of data and removal of undesirable data, tools of modelling are directly applied on a final data set. It is especially important at this age of greater data sets and Greater Data.
4. Modelling: There are various methods of modelling as decision trees, the introduction corrected, a reasoning of a basis of a case, methods of the visualization, the nearest adjacent techniques, grouping algorithms, etc. Is better the satisfied techniques of modelling is selected, Models are incorporated with various parameters, they are compared and estimated for validity and accuracy.
5. An Assessment: Models and steps in modelling are checked up with business objectives.
6. Development: Depending on an assessment and the review of process, the message is ready, or the new project of search of data once again is adjusted.
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The technique that is used to perform these feats in data mining is called Modelling. Modelling is simply the act of building a model in one situation where you know the answer and then applying it to another situation that you don't.
For instance, if you were looking for a Sunken Spanish galleon on the high seas the first thing you might do is to research the times when Spanish treasure had been found by others in the past.
You might note that these ships often tend to be found off the coast of Bermuda and that there are certain characteristics to the ocean currents, and certain routes that have likely been taken by the ship’s captains in that era.
You note these similarities and build a model that includes the characteristics that are common to the locations of these sunken treasures. With these models in hand you flow off looking for treasure where your model indicates it most likely might be given a similar situation in the past. Hopefully, if you've got a good model, you find your treasure.
This act of model building is thus something that people have been doing for a long time, certainly before the advent of computers or data mining technology.
What happens on computers, however, is not much different than the way people build models. Computers are loaded up with lots of information about a variety of situations where an answer is known and then the data mining software on the computer must run through that data and distil the characteristics of the data that should go into the model. Once the model is built it can then be used in similar situations where you don't know the answer.
What happens on computers, however, is not much different than the way people build models. Computers are loaded up with lots of information about a variety of situations where an answer is known and then the data mining software on the computer must run through that data and distil the characteristics of the data that should go into the model. Once the model is built it can then be used in similar situations where you don't know the answer.
For example, Say that you are the director of marketing for a telecommunications company and you'd like to acquire some new long distance phone customers.
You could just randomly go out and mail coupons to the general population - just as you could randomly sail the seas looking for sunken treasure. In neither case would you achieve the results you desired and of course you have the opportunity to do much better than random - you could use your business experience stored in your database to build a model.
You could just randomly go out and mail coupons to the general population - just as you could randomly sail the seas looking for sunken treasure. In neither case would you achieve the results you desired and of course you have the opportunity to do much better than random - you could use your business experience stored in your database to build a model.
As the marketing director you have access to a lot of information about all of your customers
Ex - Their age, sex, credit history and long distance calling usage.
The good news is that you also have a lot of information about your prospective customers: their age, sex, credit history etc. Your problem is that you don't know the long distance calling usage of these prospects (since they are most likely now customers of your competition). You'd like to concentrate on those prospects who have large amounts of long distance usage. You can accomplish this by building a model. Table 2 illustrates the data used for building a model for new customer prospecting in a data warehouse.
Ex - Their age, sex, credit history and long distance calling usage.
The good news is that you also have a lot of information about your prospective customers: their age, sex, credit history etc. Your problem is that you don't know the long distance calling usage of these prospects (since they are most likely now customers of your competition). You'd like to concentrate on those prospects who have large amounts of long distance usage. You can accomplish this by building a model. Table 2 illustrates the data used for building a model for new customer prospecting in a data warehouse.
Customers | Prospects | |
General information (e.g. demographic data) | Known | Known |
Proprietary information (e.g. customer transactions) | Known | Target |
Table 2 - Data Mining for Prospecting
The goal in prospecting is to make some calculated guesses about the information in the lower right hand quadrant based on the model that we build going from Customer General Information to Customer Proprietary Information.
For instance, a simple model for a telecommunications company might be:
For instance, a simple model for a telecommunications company might be:
98% of their customers who make more than $60,000/year & Spend more than $80/month on long distance
This model could then be applied to the prospect data to try to tell something about the proprietary information that this telecommunications company does not currently have access to.
With this model in hand new customers can be selectively targeted.
With this model in hand new customers can be selectively targeted.
Test marketing is an excellent source of data for this kind of modelling. Mining the results of a test market representing a broad but relatively small sample of prospects can provide a foundation for identifying good prospects in the overall market.
Table 3 shows another common scenario for building models: predict what is going to happen in the future.
Table 3 shows another common scenario for building models: predict what is going to happen in the future.
Yesterday | Today | Tomorrow | |
Static information and current plans (e.g. demographic data, marketing plans) | Known | Known | Known |
Dynamic information (e.g. customer transactions) | Known | Known | Target |
Table 3 - Data Mining for Predictions
If someone told you that he had a model that could predict customer usage how would you know if he really had a good model?
The first thing you might try would be to ask him to apply his model to your customer base - where you already knew the answer.
With data mining, the best way to accomplish this is by setting aside some of your data in a vault to isolate it from the mining process. Once the mining is complete, the results can be tested against the data held in the vault to confirm the model’s validity. If the model works, its observations should hold for the vaulted data.
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The first thing you might try would be to ask him to apply his model to your customer base - where you already knew the answer.
With data mining, the best way to accomplish this is by setting aside some of your data in a vault to isolate it from the mining process. Once the mining is complete, the results can be tested against the data held in the vault to confirm the model’s validity. If the model works, its observations should hold for the vaulted data.
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