Data Mining is the process of extracting previously unknown, comprehensible and actionable information from large databases and using it to make crucial business decisions - Simoudis 1996.
This data mining definition has business flavor and for business environments. However, data mining is a process that can be applied to any type of data ranging from weather forecasting, electric load prediction, product design, etc.
Data mining also can be defined as the computer-aid process that digs and analyzes enormous sets of data and then extracting the knowledge or information out of it. By its simplest definition, data mining automates the detections of relevant patterns in database.
Data mining Architecture
Data mining is described as a process of discover or extracting interesting knowledge from large amounts of data stored in multiple data sources such as file systems, databases, data warehouses and etc. This knowledge contributes a lot of benefits to business strategies, scientific, medical research, governments and individual.
Data is collected explosively every minute through business transactions and stored in relational database systems. In order to provide insight about the business processes, data warehouse systems have been built to provide analytical reports for business users to make decisions. Data is now stored in database and/or data warehouse system so data mining system should be designed to decouple or couple with these systems. This question leads to four possible architectures of a data mining system as follows:
- No-coupling: in this architecture, data mining system does not utilize any functionality of a database or data warehouse system. A no-coupling data mining system retrieves data from a particular data sources such as file system, processes data using major data mining algorithms and stores results into file system. The no-coupling data mining architecture does not take any advantages of database or data warehouse that is already very efficient in organizing, storing, accessing and retrieving data. The no-coupling architecture is considered a poor architecture for data mining system however it is used for simple data mining processes.
- Loose Coupling: in this architecture, data mining system uses database or data warehouse for data retrieval. In loose coupling data mining architecture, data mining system retrieves data from database or data warehouse, processes data using data mining algorithms and stores the result in those systems. This architecture is mainly for memory-based data mining system that does not require high scalability and high performance.
- Semi-tight Coupling: in semi-tight coupling data mining architecture, beside linking to database or data warehouse system, data mining system uses several features of database or data warehouse systems to perform some data mining tasks including sorting, indexing, aggregation…etc. In this architecture, some intermediate result can be stored in database or data warehouse system for better performance.
- Tight Coupling: in tight coupling data mining architecture, database or data warehouse is treated as an information retrieval component of data mining system using integration. All the features of database or data warehouse are used to perform data mining tasks. This architecture provides system scalability, high performance and integrated information.
There are three tiers in the tight-coupling data mining architecture:
- Data layer: as mentioned above, data layer can be database and/or data warehouse systems. This layer is an interface for all data sources. Data mining results are stored in data layer so it can be presented to end-user in form of reports or other kind of visualization.
- Data mining application layer is used to retrieve data from database. Some transformation routine can be performed here to transform data into desired format. Then data is processed using various data mining algorithms.
- Front-end layer provides intuitive and friendly user interface for end-user to interact with data mining system. Data mining result presented in visualization form to the user in the front-end layer.
Data Mining Applications in Sales/Marketing
Data mining enables the businesses to understand the patterns hidden inside past purchase transactions, thus helping in plan and launch new marketing campaigns in prompt and cost effective way. The following illustrates several data mining applications in sale and marketing.
Data mining is used for market basket analysis to provides insight information on what product combinations were purchased, when they were bought and in what sequence by customers. This information helps businesses to promote their most profitable products to maximize the profit. In addition, it encourages customers to purchase related products that they may have been missed or overlooked.
Retails companies uses data mining to identify customer’s behavior buying patterns.
Data Mining Applications in Banking / Finance
Several data mining techniques such as distributed data mining has been researched, modeled and developed to help credit card fraud detection.
Data mining is used to identify customers loyalty by analyzing the data of customer’s purchasing activities such as the data of frequency of purchase in a period of time, total monetary value of all purchases and when was the last purchase. After analyzing those dimensions, the relative measure is generated for each customer. The higher of the score, the more relative loyal the customer is.
To help bank to retain credit card customers, data mining is used. By analyzing the past data, data mining can help banks to predict customers that likely to change their credit card affiliation so they can plan and launch different special offers to retain those customers.
Credit card spending by customer groups can be identified by using data mining.
The hidden correlation’s between different financial indicators can be discovered by using data mining.
From historical market data, data mining enable to identify stock trading rules.
Data Mining Applications in Health Care and Insurance
The growth of the insurance industry is entirely depends on the ability of converting data into the knowledge, information or intelligence about customers, competitors and its markets. Data mining is applied in insurance industry lately but brought tremendous competitive advantages to the companies who have implemented it successfully. The data mining applications in insurance industry are listed below:
Data mining is applied in claims analysis such as identifying which medical procedures are claimed together.
Data mining enables to forecasts which customers will potentially purchase new policies.
Data mining allows insurance companies to detect risky customers’ behavior patterns.
Data mining helps detect fraudulent behavior.
Data Mining Applications in Medicine
Data mining enables to characterize patient activities to see coming office visits.
Data mining help identify the patterns of successful medical therapies for different illnesses.
Advantages of Data Mining
Marketing / Retail
Data mining helps marketing companies to build models based on historical data to predict who will respond to new marketing campaign such as direct mail, online marketing campaign and etc. Through this prediction, marketers can have appropriate approach to sell profitable products to targeted customers with high satisfaction.
Data mining brings a lot of benefit s to retail company in the same way as marketing. Through market basket analysis, the store can have an appropriate production arrangement in the way that customers can buy frequent buying products together with pleasant. In addition, it also help the retail company offers a certain discount for particular products what will attract customers.
Finance / Banking
Data mining gives financial institutions information about loan information and credit reporting. By building a model from previous customer’s data with common characteristics, the bank and financial can estimate what are the god and/or bad loans and its risk level. In addition, data mining can help banks to detect fraudulent credit card transaction to help credit card’s owner prevent their losses.
By applying data mining in operational engineering data, manufacturers can detect faulty equipments and determine optimal control parameters. For example semi-conductor manufacturers had a challenge that even the conditions of manufacturing environments at different wafer production plants are similar, the quality of wafer are lot the same and some for unknown reasons even contain defects. Data mining has been applied to determine the ranges of control parameters that lead to the production of golden wafer. Then those optimal control parameters are used to manufacture wafers with desired quality.
Disadvantages of data mining
The concerns about the personal privacy have been increasing enormously recently especially when internet is booming with social networks, e-commerce, forums, blogs…. Because of privacy issues, people are afraid of their personal information is collected and used in unethical way that potentially causing them a lot of trouble. Businesses collect information about their customers in many ways for understanding their purchasing behaviors trends. However businesses don’t last forever, some days they may be acquired by other or gone. At this time the personal information they own probably is sold to other or leak.
Security is a big issue. Businesses owns information about their employee and customers including social security number, birthday, payroll and etc. However how properly this information is taken is still in questions. There have been a lot of cases that hackers were accesses and stole big data of customers from big corporation such as Ford Motor Credit Company, Sony… with so much personal and financial information available, the credit card stolen and identity theft become a big problem.
Misuse of information/inaccurate information
Information collected through data mining intended for marketing or ethical purposes can be misused. This information is exploited by unethical people or business to take benefit of vulnerable people or discriminate against a group of people.
In addition, data mining technique is not perfectly accurate therefore if inaccurate information is used for decision-making will cause serious consequence.
Data Mining techniques
There are several major data mining techniques have been developed and used in data mining projects recently including association, classification, clustering, prediction and sequential patterns. We will briefly examine those data mining techniques to help you have a good overview of each .
Association is one of the best known data mining technique. In association, a pattern is discovered based on a relationship of a particular item on other items in the same transaction. For example, the association technique is used in market basket analysis to identify what products that customers frequently purchase together. Based on this data businesses can have corresponding marketing campaign to sell more products to make more profit.
Classification is a classic data mining technique based on machine learning. Basically classification is used to classify each item in a set of data into one of predefined set of classes or groups. Classification method makes use of mathematical techniques such as decision trees, linear programming, neural network and statistics. In classification, we make the software that can learn how to classify the data items into groups. For example, we can apply classification in application that “given all past records of employees who left the company, predict which current employees are probably to leave in the future.” In this case, we divide the employee’s records into two groups that are “leave” and “stay”. And then we can ask our data mining software to classify the employees into each group.
Clustering is a data mining technique that makes meaningful or useful cluster of objects that have similar characteristic using automatic technique. Different from classification, clustering technique also defines the classes and put objects in them, while in classification objects are assigned into predefined classes. To make the concept clearer, we can take library as an example. In a library, books have a wide range of topics available. The challenge is how to keep those books in a way that readers can take several books in a specific topic without hassle. By using clustering technique, we can keep books that have some kind of similarities in one cluster or one shelf and label it with a meaningful name. If readers want to grab books in a topic, he or she would only go to that shelf instead of looking the whole in the library.
The prediction as it name implied is one of a data mining techniques that discovers relationship between independent variables and relationship between dependent and independent variables. For instance, prediction analysis technique can be used in sale to predict profit for the future if we consider sale is an independent variable, profit could be a dependent variable. Then based on the historical sale and profit data, we can draw a fitted regression curve that is used for profit prediction.
Sequential patterns analysis in one of data mining technique that seeks to discover similar patterns in data transaction over a business period. The uncover patterns are used for further business analysis to recognize relationships among data.