Data Mining Tutorial – Introduction to Data Mining (Complete Guide)
In this Data Mining Tutorial, we will study what is Data Mining. Also, will study data mining scope, foundation, data mining techniques and terminologies in Data Mining. As we study this, will learn data mining architecture with a diagram.
Further, will study knowledge discovery. Along with we will also learn data mining applications and pros and cons.
So, let’s start the Data Mining Tutorial.
What is Data Mining?
Data Mining is a set of method that applies to large and complex databases. This is to eliminate the randomness and discover the hidden pattern. As these data mining methods are almost always computationally intensive.
We use data mining tools, methodologies, and theories for revealing patterns in data. There are too many driving forces present. And, this is the reason why data mining has become such an important area of study.
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Data Mining History
In 1960s statisticians used the terms “Data Fishing” or “Data Dredging”. That was to refer to what they considered the bad practice of analyzing data. The term “Data Mining” appeared around 1990 in the database community.
Data Mining Foundation
We use data mining techniques for a long process of research and product development. As this evolution was started when business data was first stored on computers.
Also, it allows users to navigate through their data in real time. We use data mining in the business community because it is supported by three technologies that are now mature:
- Massive data collection
- Powerful multiprocessor computers
- Data mining algorithms
Why Data Mining?
As data mining is having spacious applications. Thus, it is the young and promising field for the present generation. It has attracted a great deal of attention in the information industry and in society.
Due to the wide availability of huge amounts of data and the imminent need for turning such data into useful information and knowledge. Thus, we use information and knowledge for applications ranging from market analysis. This is the reason why data mining, known as knowledge discovery from data.
Type of Data Gathered
In this part of the Data Mining Tutorial, we will discuss the types of data gathered in data mining:
a. Business Transactions
In this business industry, every transaction is “memorized” for perpetuity. We can say many transactions are dealing with time and can be inter-business deals such as purchases, exchanges, banking, stock, etc.,
b. Scientific Data
Everywhere, our society is amassing colossal amounts of scientific data. As that scientific data need to be analyzed. Unfortunately, we have to capture and store more new data faster. Then we can analyze the old data already accumulated.
c. Medical and Personal Data
As we can say from the government to customer and for personal needs, we have to gather large information. That information is required for individuals and groups.
When correlated with other data, this information can shed light on customer behaviour.
d. Surveillance Video and Pictures
As with the collapse of video camera prices, video cameras are becoming ubiquitous. Also, we can recycle cameras, videotapes from surveillance. However, it’s become a trend to store the tapes and even digitize them for future use and analysis.
In societies, a huge amount of data and statistics is used. That is to collect games, players, and athletes. As this information data is used by commentators and journalists for reporting.
f. Digital Media
There are too many reasons for causes of the explosion in digital media repositories. Such as cheap scanners, desktop video cameras, and digital cameras. Associations such as the NHL and the NBA. That has already started converting their huge game collection into digital forms.
g. CAD and Software Engineering Data
There are multiple CAD systems for architects present to design building. As these systems are used to generate a huge amount of data.
Moreover, we can use S.E is a source of considerable similar data with code and objects that needs to be powerful tools for management and maintenance.
h. Virtual Worlds
Nowadays many applications are using three-dimensional virtual spaces. Also, these spaces and the objects they contain have to describe with special languages such as VRML. Ideally, we have to define virtual spaces as they can share objects and places. Also, there present the remarkable amount of virtual reality object available.
i. Text reports and memos (e-mail messages)
As communications are based on the reports and memos in textual forms in many companies. As they are exchanged by e-mail. Although, we use to store it in digital form for future use. Also, reference creating formidable digital libraries.
Uses of Data Mining
Following are the uses of Data Mining, let’s discuss them one by one:
a. Automated Prediction of Trends and behaviours
We use to automate the process of finding predictive information in large databases. Questions that required extensive hands-on analysis can now be answered from the data.
Targeted marketing is a typical example of predictive marketing. As we also use data mining on past promotional mailings. That is to identify the targets to maximize return on investment in future mailings.
Other predictive problems include forecasting bankruptcy and other forms of default. And identifying segments of a population likely to respond similarly to given events.
b. Automated Discovery of Previously Unknown Patterns
As we use data mining tools to sweep through databases. Also, to identify previously hidden patterns in one step. There is a very good example of pattern discovery. As it is the analysis of retail sales data. That to identify unrelated products that often purchase together.
Also, there are other pattern discovery problems. That includes detecting fraudulent credit card transactions. It is identified that anomalous data could represent data entry keying errors.
Data Mining Techniques
Here, in this session of Data Mining Tutorial, we will explore the techniques used in Data Mining:
a. Artificial Neural Networks
We use data mining in non-linear predictive models. As this learn through training and resemble biological neural networks in structure.
b. Decision Trees
As we use tree-shaped structures to represent sets of decisions. Also, these rules are generated for the classification of a dataset. These decisions generate rules for the classification of a dataset.
As there are specific decision tree methods that include Classification and Regression Trees and Chi-Square Automatic Interaction Detection (CHAID).
c. Genetic Algorithms
There are the present genetic combination, mutation, and natural selection for optimization techniques. That is design based on the concepts of evolution.
d. Nearest Neighbor Method
A technique that classifies each record in a dataset based on a combination of the classes of the k record(s) like. it in a historical dataset (where k ³ 1). Sometimes called the k-nearest neighbour technique.
e. Rule Induction
The extraction of useful if-then rules from data based on statistical significance.
Data Mining Terminologies
In this Data Mining Tutorial, we will learn some basic and important terms used in Data Mining:
Input X: X is often multidimensional.
Each dimension of X is denoted by Xj and is referred to as a feature variable or, variable.
Output Y: called the response or dependent variable.
A response is available only when learning is supervised.
b. Nature of Data Sets
i. Quantitative: Measurements or counts, recorded as numerical values, e.g. Height, Temperature, # of Red M&M’s in a bag.
ii. Qualitative: Group or categories
iii. Ordinal: Possesses a natural ordering, e.g. Shirt sizes (S, M, L, XL)
iv. Nominal: Just name of the categories, e.g. Marital Status, Gender,
Color of M&M’s in a bag
Data Mining Architecture
We need to apply advanced techniques in the best way. As they must be fully integrated with a data business analysis tools. To operate data mining tools we need extra steps for the extracting, and importing the data.
Furthermore, new insights need operational implementation, integration with the warehouse simplifies the application. We have to apply an analytic data warehouse to improve business processes. Particularly in areas such as promotional campaign management, and so on.
The ideal starting point is a data warehouse that must contain a combination of internal data tracking all customer contact. This should couple with external market data about competitor activity. Background information on potential customers also provides an excellent basis for prospecting.
An OLAP (On-Line Analytical Processing) server enables a more sophisticated end-user business model. That need to apply when navigating the data warehouse. Although, multidimensional structures allow the user to analyze the data. As they want to view their business. Such as summarizing by product line, region.
Further, the Data Mining Server must be integrated with the data warehouse. And, the OLAP server to embed ROI-focused business analysis directly into this infrastructure. Also, integration with the data warehouse enables the operational decisions. That is to be implemented and tracked.
Also, keep the warehouse grows with new decisions and results. Thus, the organization can mine the best practices and apply them to future decisions
In the OLAP, results enhance the metadata. That is by providing a dynamic metadata layer. As this layer is used to represents a distilled view of the data. Reporting, visualization, and tools can then be applied to plan future actions. And confirm the impact of those plans.
Data Mining Process
Data Mining, also popularly known as Knowledge Discovery in Databases (KDD)). Also, nontrivial extraction of implicit information from data in databases.
This Data Mining process comprises of a few steps. That is to lead from raw data collections to some form of new knowledge. The iterative process consists of the following steps:
a. Data Cleaning
In this phase noise data and irrelevant data are removed from the collection.
b. Data Integration
In this multiple data is combined at the same place.
c. Data Selection
We have to decide the data relevant to the analysis is decided on and retrieved from the data collection.
d. Data Transformation
It is also a data consolidation method. Also, it’s a phase in which the selected data is transformed into forms. That is appropriate for the mining procedure.
e. Data Mining
In this, we have to apply clever techniques to extract patterns potentially useful.
f. Pattern Evaluation
In this process, interesting patterns representing knowledge are identified based on given measures.
g. Knowledge Representation
It is the final phase. Particularly in this phase, knowledge is discovered and represented to the user. This essential step uses visualization techniques. That help users understand and interpret the data mining results.
Categories of Data Mining Systems
As there are too many data mining systems available, but in this Data Mining Tutorial, we will study 4 major classifications. Also, some systems are specific that we need to dedicate to a given data source. Further, according to various criteria, data mining systems have to categorize.
a. Classification according to the type of data source mined
According to the type of data handle, have to perform classification of data mining. Such as spatial data, multimedia data, time-series data, text data, World Wide Web, etc.
b. Classification according to the data model drawn on
In this classification is done on the basis of a data model. Such as a relational database, object-oriented database, data warehouse, transactional, etc.
c. Classification according to the king of knowledge discovered
In this classification, it is been done on the basis of the kind of knowledge. Such as characterization, discrimination, association, classification, clustering, etc.
d. Classification according to mining techniques used
As data mining systems employ are used to provide different techniques. According to the data analysis, we have to do this classification. Such as machine learning, neural networks, genetic algorithms, etc.
Data Mining Issues
In this part of the Data Mining Tutorial, we will discuss some major issues we faced in it.
a. Mining Methodology Issues
These issues to the data mining approach applied and their limitations such as the versatility of the mining approaches that can dictate mining methodology choices.
b. Performance Issues
As there is much artificial intelligence and statistical methods exist. That is use for data analysis. However, these methods were often not designed for the very large datasets. And data mining is dealing with today. As Terabyte sizes are common.
We can say this raises the issues of scalability and efficiency of the data mining methods. That would process considerably large data. . Moreover, Linear algorithms are usually the norm. In the same theme, sampling can be used for mining instead of the whole dataset.
However, issues like completeness and choice of samples may arise. Other topics in the issue of performance are incremental updating and parallel programming. We use parallelism to solve the size problem. And if the dataset can be subdivided and the results can be merged later.
Incremental updating is important for merging results from parallel mining. That the new data becomes available without having to re-analyze the complete dataset.
c. Data Source Issues
We must know that there are many issues related to the data sources. Some are practical such as the diversity of data types. While others are philosophical like the data glut problem.
We certainly have an excess of data since. Also, we already have more data than we can handle. Then we are still collecting data at an even higher rate. Although, If the spread of database management systems.
That has helped in increasing the gathering of information. And the advent of data mining is certainly encouraging more data harvesting. The current practice is to collect as much data as possible now and process it or try to process it, later.
Regarding the practical issues related to data sources, there is the subject databases. Thus, we need to focus on diverse complex data types. We are storing different types of data in a variety of repositories. It is difficult to expect a data mining system to achieve good mining results on all kinds of data and sources.
As different kinds of data and sources may require distinct algorithms and methodologies. Currently, there is a focus on relational databases and data warehouses.
It’s a versatile data mining tool, for all sorts of data, may not be realistic. Moreover, data sources, at structural and semantic levels, poses important challenges. That is not only to the database community but also to the data mining community.
Data Mining Applications
- Weather forecasting.
- Self-driving cars.
- Hazards of new medicine.
- Space research.
- Fraud detection.
- Stock trade analysis.
- Business forecasting.
- Social networks.
- Customers likelihood.
More applications include:
- A credit card company can leverage its vast warehouse of customer transaction data. As we perform this to identify customers. It shows more interest in a new credit product.
- Moreover, we use small test mailing. So the attributes of customers with an affinity for the product have to identify. Recent projects have indicated more than a 20-fold decrease in costs. That is a target for mailing campaigns over conventional approaches.
- As a diversified transportation company used to apply data mining. That is to identify the best prospects for its services. Further, need to apply this segmentation to a general business database. Such as those provided by Dun & Bradstreet can yield a prioritized list of prospects by region.
- Large consumer packaged goods company. That can apply data mining to improve its sales process to retailers. Although, data from consumer panel and competitor activity have to apply. That is to understand the reasons for brand and store switching.
- Through this analysis, we have to the manufacturer it. Then select promotional strategies that best reach their target customer segments.
Areas where Data Mining had Good and Bad Effects
a. Good Effects
- Predict future trends, customer purchase habits
- Help with decision making
- Improve company revenue and lower costs
- Market basket analysis
- Fraud detection
b. Bad Effects
- User privacy/security
- Amount of data is overwhelming
- Great cost at an implementation stage
- Possible misuse of information
- The possible inaccuracy of data
Data Mining Advantages and Disadvantages
a. Data Mining Advantages
- To find probable defaulters, we use data mining in banks and financial institutions. This is done based on past transactions, user behavior and data patterns.
- It helps advertisers to push right advertisements to the internet. That surfer on web pages based on machine learning algorithms. This way data mining benefit both possible buyers as well as sellers of the various products.
- The retail malls and grocery stores peoples used data mining. That is to arrange and keep most sellable items in the most attentive positions. It has become possible due to inputs obtained from data mining software. This way data mining helps in increasing revenue.
- As data mining is having different methods. That is cost-effective compared to other applications.
- We use data mining in so many areas. Such as bio-informatics, medicine, genetics, etc.
- We use data mining to identifying criminal suspects. That is by law enforcement agencies as mentioned above.
b. Data Mining Disadvantages
- Security: The time at which users are online for various uses, must be important. They do not have security systems in place to protect us.
- As some of the data mining analytics use software. That is difficult to operate. Thus they require a user to have knowledge based training.
- The techniques of data mining are not 100% accurate. Hence, it may cause serious consequences in certain conditions.
So, this was all about Data Mining Tutorial. Hope you like our explanation.
As a result, we have studied Data Mining introduction. Also, have studied about it’s all concepts. We have covered each and everything with pros-cons and applications. Furthermore, if you feel any query regarding Data Mining tutorial, feel free to ask in a comment section.