Overview Generally, data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase.
Microsoft SQL Server Analysis Services provides the following tools that you can use to create data mining solutions. Six of the Best Open Source Data Mining Tools. With the Java-based version, the tool is very sophisticated and used in many different applications including visualization and algorithms for data analysis and predictive. Examine different data mining and analytics techniques and solutions. Learn how to build them using existing software and installations. A combination of different data sets, e.g. patents and publications. Because the format of the data is not predetermined, it needs some preparation before. Data Mining Tools for Technology and Competitive Intelligence. A Comparison Study between Data Mining Tools over some Classification Methods Abdullah H. Wahbeh. various data mining methods ranging from different types of algorithms that are supported by the five tools, these includes. Understanding the advantages of using different data mining tools and techniques — and knowing what data mining does — can help beginner auditors provide recommendations that improve business processes and discover fraud.
Data mining tools are software tools used to extract information from data. One of the most common uses of data mining tools in. The book Data mining: Practical machine learning tools and. but the same problem can arise at different phases of. Several researchers and organizations have conducted reviews of data mining tools and surveys of data.
Data Mining 1. 01: Tools and TechniquesMost internal auditors, especially those working in customer- focused industries, are aware of data mining and what it can do for an organization — reduce the cost of acquiring new customers and improve the sales rate of new products and services. However, whether you are a beginner internal auditor or a seasoned veteran looking for a refresher, gaining a clear understanding of what data mining does and the different data mining tools and techniques available for use can improve audit activities and business operations across the board. What is Data Mining? In its simplest form, data mining automates the detection of relevant patterns in a database, using defined approaches and algorithms to look into current and historical data that can then be analyzed to predict future trends.
Because data mining tools predict future trends and behaviors by reading through databases for hidden patterns, they allow organizations to make proactive, knowledge- driven decisions and answer questions that were previously too time- consuming to resolve. Data mining is not particularly new — statisticians have used similar manual approaches to review data and provide business projections for many years. Changes in data mining techniques, however, have enabled organizations to collect, analyze, and access data in new ways. The first change occurred in the area of basic data collection. Before companies made the transition from ledgers and other paper- based records to computer- based systems, managers had to wait for staff to put the pieces together to know how well the business was performing or how current performance periods compared with previous periods. As companies started collecting and saving basic data in computers, they were able to start answering detailed questions quicker and with more ease.
An Overview of Data Mining Techniques. Excerpted from the book Building Data. techniques and at least enough information to be dangerous and well armed enough to not be baffled by the vendors of different data mining tools.
Changes in data access — where there has been greater empowerment and integration, particularly over the past 3. The introduction of microcomputers and networks, and the evolution of middleware, protocols, and other methodologies that enable data to be moved seamlessly among programs and other machines, allowed companies to link certain data questions together. The development of data warehousing and decision support systems, for instance, has enabled companies to extend queries from "What was the total number of sales in New South Wales last April?" to "What is likely to happen to sales in Sydney next month, and why?"However, the major difference between previous and current data mining efforts is that organizations now have more information at their disposal.
Given the vast amounts of information that companies collect, it is not uncommon for them to use data mining programs that investigate data trends and process large volumes of data quickly. Users can determine the outcome of the data analysis by the parameters they chose, thus providing additional value to business strategies and initiatives. It is important to note that without these parameters, the data mining program will generate all permutations or combinations irrespective of their relevance.
Internal auditors need to pay attention to this last point: Because data mining programs lack the human intuition to recognize the difference between a relevant and an irrelevant data correlation, users need to review the results of mining exercises to ensure results provide needed information. For example, knowing that people who default on loans usually give a false address might be relevant, whereas knowing they have blue eyes might be irrelevant.
Auditors, therefore, should monitor whether sensible and rational decisions are made on the basis of data mining exercises, especially where the results of such exercises are used as input for other processes or systems. Auditors also need to consider the different security aspects of data mining programs and processes. A data mining exercise might reveal important customer information that could be exploited by an outsider who hacks into the rival organization's computer system and uses a data mining tool on captured information.
Data Mining Tools. Organizations that wish to use data mining tools can purchase mining programs designed for existing software and hardware platforms, which can be integrated into new products and systems as they are brought online, or they can build their own custom mining solution. For instance, feeding the output of a data mining exercise into another computer system, such as a neural network, is quite common and can give the mined data more value. This is because the data mining tool gathers the data, while the second program (e. Different types of data mining tools are available in the marketplace, each with their own strengths and weaknesses. Internal auditors need to be aware of the different kinds of data mining tools available and recommend the purchase of a tool that matches the organization's current detective needs. This should be considered as early as possible in the project's lifecycle, perhaps even in the feasibility study.
Most data mining tools can be classified into one of three categories: traditional data mining tools, dashboards, and text- mining tools. Below is a description of each. Traditional Data Mining Tools. Traditional data mining programs help companies establish data patterns and trends by using a number of complex algorithms and techniques.
Some of these tools are installed on the desktop to monitor the data and highlight trends and others capture information residing outside a database. The majority are available in both Windows and UNIX versions, although some specialize in one operating system only. In addition, while some may concentrate on one database type, most will be able to handle any data using online analytical processing or a similar technology. Dashboards. Installed in computers to monitor information in a database, dashboards reflect data changes and updates onscreen — often in the form of a chart or table — enabling the user to see how the business is performing. Historical data also can be referenced, enabling the user to see where things have changed (e. This functionality makes dashboards easy to use and particularly appealing to managers who wish to have an overview of the company's performance.
Text- mining Tools. The third type of data mining tool sometimes is called a text- mining tool because of its ability to mine data from different kinds of text — from Microsoft Word and Acrobat PDF documents to simple text files, for example. These tools scan content and convert the selected data into a format that is compatible with the tool's database, thus providing users with an easy and convenient way of accessing data without the need to open different applications. Scanned content can be unstructured (i. Internet pages, audio and video data) or structured (i. Capturing these inputs can provide organizations with a wealth of information that can be mined to discover trends, concepts, and attitudes. Besides these tools, other applications and programs may be used for data mining purposes. For instance, audit interrogation tools can be used to highlight fraud, data anomalies, and patterns.
An example of this has been published by the United Kingdom's Treasury office in the 2. Fraud Report: Anti- fraud Advice and Guidance, which discusses how to discover fraud using an audit interrogation tool. Additional examples of using audit interrogation tools to identify fraud are found in David G. Coderre's 1. 99. 9 book, Fraud Detection.
In addition, internal auditors can use spreadsheets to undertake simple data mining exercises or to produce summary tables. Some of the desktop, notebook, and server computers that run operating systems such as Windows, Linux, and Macintosh can be imported directly into Microsoft Excel.
Using pivotal tables in the spreadsheet, auditors can review complex data in a simplified format and drill down where necessary to find the underlining assumptions or information. When evaluating data mining strategies, companies may decide to acquire several tools for specific purposes, rather than purchasing one tool that meets all needs. Although acquiring several tools is not a mainstream approach, a company may choose to do so if, for example, it installs a dashboard to keep managers informed on business matters, a full data- mining suite to capture and build data for its marketing and sales arms, and an interrogation tool so auditors can identify fraud activity.
Data Mining Techniques and Their Application. In addition to using a particular data mining tool, internal auditors can choose from a variety of data mining techniques. The most commonly used techniques include artificial neural networks, decision trees, and the nearest- neighbor method. Each of these techniques analyzes data in different ways: Artificial neural networks are non- linear, predictive models that learn through training. Although they are powerful predictive modeling techniques, some of the power comes at the expense of ease of use and deployment. One area where auditors can easily use them is when reviewing records to identify fraud and fraud- like actions. Because of their complexity, they are better employed in situations where they can be used and reused, such as reviewing credit card transactions every month to check for anomalies.
Decision trees are tree- shaped structures that represent decision sets. These decisions generate rules, which then are used to classify data. Decision trees are the favored technique for building understandable models. Auditors can use them to assess, for example, whether the organization is using an appropriate cost- effective marketing strategy that is based on the assigned value of the customer, such as profit.
The nearest- neighbor method classifies dataset records based on similar data in a historical dataset. Auditors can use this approach to define a document that is interesting to them and ask the system to search for similar items.
An Overview of Data Mining Techniques. An Overview. of Data Mining Techniques. Excerpted from the book. Building. Data Mining Applications for CRMby Alex Berson, Stephen Smith, and Kurt Thearling. Introduction. This overview provides a description of some of the most. We have broken the. Classical Techniques: Statistics, Neighborhoods and.
Clustering. Next Generation Techniques: Trees, Networks and Rules. Each section will describe a number of data mining. Overall, six broad classes of data mining. Although there are a number of other algorithms and many.
I. Classical Techniques: Statistics, Neighborhoods and Clustering. The Classics. These two sections have been broken up based on when the. Thus this section contains descriptions. This section should help the user to understand the rough. The main techniques that we will discuss here are the.
There. are certainly many other ones as well as proprietary techniques from particular. Statistics. By strict definition "statistics" or. They were being used long. However, statistical techniques are driven by the data and are used to discover. And from the users perspective you. For this reason it is important to have some idea.
What is different between statistics and. I flew the Boston. Boston area. Universities. He was going to discuss the drosophila (fruit flies). New Jersey. He had compiled the world's largest database on the genetic makeup. Java applications accessing a larger relational database. He explained to me that they not only now were storing.
I mentioned that I had written a book on the subject and he was. There was no easy answer. The techniques used in data mining, when successful, are. And for the most part the techniques are. In fact some of the techniques that are.
CART and CHAID arose from. So what is the difference? Why aren't we as excited. There are. several reasons. The first is that the classical data mining techniques. CART, neural networks and nearest neighbor techniques tend to be more.
But that is not the only reason. The other reason. Because of the use of computers for closed loop. IF there were no data - there would be no. Likewise the fact that computer hardware has.
The bottom line though, from an academic standpoint at. Hence we have included a. What is statistics? Statistics is a branch of mathematics concerning the.
Usually statistics is considered. However, statistics is probably a much friendlier branch of. Statistics was in. Knowing statistics in your everyday life will help.
Even with all the data stored in the largest of data warehouses. The more and. better the data and the better the understanding of statistics the better the.
Statistics has been around for a long time easily a. It could even be argued that the data collected by the ancient Egyptians. Babylonians, and Greeks were all statistics long before the field was officially. Today data mining has been defined independently of statistics. Some of the techniques that are classified under data mining.
CHAID and CART really grew out of the statistical profession more than. Data, counting and probability. One thing that is always true about statistics is that. This is. certainly more true today than it was when the basic ideas of probability and. Today. people have to deal with up to terabytes of data and have to make sense of it. Statistics can help greatly in. What patterns are.
What is the. chance that an event will occur? Which patterns. are significant? What is a high. level summary of the data that gives me some idea of what is contained in my. Certainly statistics can do more than answer these. Consider for example that a large part of statistics is. One of the great values of statistics is in.
This aspect of statistics is the part that people run into every day when they. US citizens of different eye colors, or the average number of annual doctor. Statistics at this level is. There are many different parts of statistics. The first step then in understanding. Histograms. One of the best ways to summarize data is to provide a.
In the simple example database shown in Table 1. For this example database of 1. However, for a database of. IDName. Prediction.
Age. Balance. Income. Eyes. Gender. 1Amy. No. 62$0. Medium. Brown. F2. Al. No. Medium. Green. M3. Betty. No. 47$1. 6,5. High. Brown. F4. Bob.
Yes. 32$4. 5Medium. Green. M5. Carla. Yes. 21$2,3. 00. High.
Blue. F6. Carl. No. High. Brown. M7. Donna. Yes. 50$1. 65. Low.
Blue. F8. Don. Yes. High. Blue. M9. Edna.
Yes. 27$5. 00. Low. Blue. F1. 0Ed. No. Low. Blue. MTable 1. An. Example Database of Customers with Different Predictor Types. This histogram shown in figure 1. There are. however, other predictors that have many more distinct values and can create a.
Consider, for instance, the histogram of ages. In this case the histogram can be more.
Consider if you found that the. Figure 1. 1 This. This. summary can quickly show important information about the database such as that.
Figure 1. 2 This histogram shows the number of customers of different ages and quickly tells. By looking at this second histogram the viewer is in many. By looking at this histogram it is also possible to build an.
Such as the average age of the. All of which are important. These values are called summary statistics. Some of the most frequently. Max - the maximum. Min - the minimum. Mean - the. average value for a given predictor. Median - the. value for a given predictor that divides the database as nearly as possible.
Mode - the most. common value for the predictor. Variance - the. measure of how spread out the values are from the average value. When there are many values for a given predictor the.
Sometimes the shape of the distribution of data. This is what is called a data distribution. Like a histogram a data. In classical. statistics the belief is that there is some true underlying shape to the. The shape of the data distribution can be calculated for some simple examples. The statisticians job then is to take the limited data that may have been.
Many data distributions are well described by just two. The mean is something most people are. The easiest way. to think about it is that it measures the average distance of each predictor. If the. variance is high it implies that the values are all over the place and very. If the variance is low most of the data values are fairly close.
To be precise the actual definition of the variance uses the. In terms of prediction a user could make some guess at the. Statistics for Prediction. In this book the term prediction is used for a. We have done so in order to simplify some of the concepts and. Nonetheless regression is a powerful and commonly used tool in statistics and it. Linear regression In statistics prediction is usually synonymous with.
There are a variety of different types of. The simplest form of regression is simple linear regression. The relationship.
Y axis and the predictor values along the X. The simple linear regression model then could be viewed as the line. Graphically this would look. Figure 1. 3. The simplest form of regression seeks to build a. Of the many possible lines that could be drawn through.
On average if you guess the value on the line it should. Likewise if there is no data available for a. Figure 1. 3 Linear. The predictive model is the line shown in Figure 1.
The line will take a given value for a predictor and map it into a given value. The actual equation would look something like. Prediction = a + b * Predictor. Which is just the equation for a line Y =. X. As an example for a bank the predicted average consumer bank.
The trick. as always with predictive modeling, is to find the model that best minimizes the. The most common way to calculate the error is the square of the. Calculated. this way points that are very far from the line will have a great effect on. The values of a and b in the regression equation that minimize this error can be. What if the pattern in my data doesn't. Regression can become more complicated than the simple. It can get more complicated.
There are, however, three main modifications that can be. More predictors than just one can be used. Transformations can be applied to the predictors. Predictors can be multiplied together and used as terms in the equation.
Modifications can be made to accommodate response predictions that just have. Adding more predictors to the linear equation can produce. This is called multiple linear regression and might.
X1, X2, X3, X4. X5): Y = a + b. X1) + b. 2(X2) + b. X3) + b. 4(X4) +.
X5)This equation still describes a line but it is now a line. By transforming the predictors by squaring, cubing or. This is called non- linear regression. A model of. Y = a + b. 1(X1) + b. X1. 2). In many real world cases analysts will perform a wide variety of transformations.
If they do not contribute to a useful. The other transformation of predictor values that is often.
For instance a new predictor. When trying to predict a customer response that is just. Since there are. only two possible values to be predicted it is relatively easy to fit a line. However, that model would be the same no matter what. Typically in these situations a transformation of the prediction values is made.
This type of regression is. Nearest Neighbor. Clustering and the Nearest Neighbor prediction technique. Most people have an. Nearest neighbor is a prediction technique.
A simple example of clustering. A simple example of clustering would be the clustering.
And it turns out they have important. To cluster your laundry most of your decisions are relatively. There are of course difficult decisions to be made about. When clustering is used. A simple example of nearest neighbor. A simple example of the nearest neighbor prediction. You may notice. that, in general, you all have somewhat similar incomes. Thus if your.
Certainly the chances that you have a high income are. Within your neighborhood there may. The nearest neighbor prediction algorithm works in very. It may, for. instance, be far more important to know which school someone attended and what. The better definition of.