In this article, you will learn about the essential aspects of Data mining. We will talk about data mining concepts, techniques, and methods. We will also discuss how data mining is necessary for business analytics, its implementation process, as well as its importance in machine learning. Read on to find out!
What is Data Mining?
Data mining can be described as the process of sorting through vast volumes of data to identify patterns and find relationships to solve issues or problems through analyzing data. Data mining enables business enterprises to predict what is likely going to happen in the future.
It is used by companies to transform raw data into meaningful information. Businesses get to learn more about their customers to develop effective marketing strategies, reduce the production cost, and increase sales. Data mining relies on warehousing, effective data collection, and computer processing.
Data mining processes are used to create machine learning models that can be used to power applications such as website recommendation platforms and search engine technology.
How does Data Mining work? It involves analyzing and exploring large sets of data to establish meaningful patterns and trends. Data mining can be used in credit risk management, database marketing, fraud detection, and spam email filtering. We will discuss more the uses of data mining later on in the article.
Having known what Data Mining is, next, we discuss its concepts, techniques, and methods. Read on!
Data Mining Concepts
Creating a data mining model is part of bigger processes that involves everything from asking questions about the data in question to developing a model that can help analyze and answer the questions. The following six steps can clearly explain the process.
i) Defining the Problem
This is the first step in the data mining process. It includes analyzing business necessities, defining the core of the problem, defining the specific objectives for the data mining process, and defining the metrics by which the model is going to be evaluated.
To answer those questions, you will have to do a data availability study to determine the needs in relation to the available data. If the data does not support the user’s needs, you will have to redefine the project. You need to put in consideration methods in which the results of models can be merged in key performance indicators that are used to determine business progress.
ii) Preparing Data
This is the second step in data mining. It is the stage where data is consolidated and cleaned. Data, in most occasions, can be scattered across an organization and stored in various formats. It may contain abnormalities like missing or incorrect entries. For instance, data might indicate that a customer purchased a product before the product was even introduced in the market.
Cleaning data isn’t just about doing away with bad data or filling up missing values, it is about finding the hidden correlations in data, identifying most accurate sources of data and establishing which columns that are most fit for use in the analysis.
For data mining, you are typically working with a large set of data and can’t determine each and every transaction for data quality. This will, therefore, need you to use data profiling and automated data filtering tools. Some of those tools are Microsoft SQL Server and SQL Server Data.
However, it is essential to note that the data used for data mining doesn’t need to be stored in Online Analytical Processing cube.
iii) Exploring Data
This is the third stage in the data mining process. In order to make appropriate decisions when you create a mining model, you must first understand the data. Some of the exploration techniques are calculating the maximum and minimum values, calculating standard deviations and mean and exploring data distribution. Standard deviations and other distribution values can give useful information about the accuracy and stability of the results.
You can employ tools like Master Data Services to establish available sources of data and determine data mining availability. You can also use tools Data Profiler and SQL Server Data Quality Services to analyze your data distribution and repair errors like missing or wrong data.
After defining your sources, you can combine them in a Data Source by applying the Data Source View Designer in SQL.
iv) Building Models
The fourth step involves building a mining model. You will apply the knowledge you amassed in Exploring Database stage to help create and define models.
To define the columns of data you prefer to use, create a mining structure. The mining structure is connected to the source of data but doesn’t have any data unless you process it. The information can be used in any kind of mining model that is based on the structure. Before structure and model processing, a data mining model is just a container that outlines the columns used for input and the parameters that tell the algorithm how to process the data.
You can also use parameters to adjust algorithms. Additionally, you can apply filters to data so as to use a subset of it, creating different results. After taking data through the model, the mining model object contains summaries that can be used for prediction.
It is important to note that whenever data changes, you should update both the mining model and the mining structure.
v) Exploring and Validating Models
This stage involves exploring the mining models, build and test their effectiveness.
Before deploying a model in a production environment, it is prudent to test how well the model performs. When building a model, you sometimes can create multiple models with various configurations and test all of them to determine which one yields the best solution to your problem.
To explore trends and patterns that the algorithms discover, use the viewers in Data Mining Designer tools. You can test how good the models create predictions by using applying tools in the designer like the lift chart and classification matrix. To determine whether the model is significant to your data or not, use the statistical technique to automatically create subsets of the data and the model against each subset.
vi) Deploying and Updating Models
This is the final step in the data mining process. This stage involves the deployment of models that performed best in a production environment. With the mining models in place, you can perform various tasks depending on your needs. Below are some of those tasks;
- Use models to create predictions that can later be used in making business decisions.
- Creating content queries to retrieve rules, statistics, or formulas from the model.
- Use integration services to create a package that the mining model is used to sort incoming data into multiple different tables intelligently.
Data Mining Techniques
Today, there are several data mining techniques used in data mining projects. We will examine those techniques in the following subsection.
Association
In this technique, a pattern is discovered based on the relationship between things in the same transaction. This is why the association technique is often referred to as a relation technique. This technique is mostly used in market analysis in order to identify the most frequently purchased set of products.
Retailers are using association techniques to discover customer’s buying habits based on historical sale data. This might help them establish that customers always buy a pack of crisps when they beers and they can, therefore, put crisps and beers next to each other in order to save the customer’s time and in the long run, increase sales.
Classification
This is a data mining technique based on machine learning. It is used to classify each item in a set of data into one large predefined set of groups. The classification technique puts into using mathematical methods like linear programming, decision trees, neural network, and statistics. In classification, you develop software that can learn how to classify data items into groups.
Clustering
It is a data mining technique that makes use of a cluster of objects that have similar characteristics using an automatic technique. It defines the classes and categorizes objects into each class. For example, in book management in a library, there is a wide range of books each talking about different topics. The challenge in this situation can be keeping those books in such a way that readers can pick several books in a particular topic without a struggle. By using the clustering technique, you can keep books that have similarities in one shelf or cluster and label it with a relatable name. So if readers want to pick books in that topic, they will only have to go to that shelf rather doing rounds in the entire library.
Prediction
Just as the name implies, the prediction is a data mining technique that discovers the link between independent variables and the relationship between independent and dependent variables. For example, the prediction technique can be applied in the sales to predict profits for the future if the sale can be considered as an independent variable with profit being the dependent variable. Based on the previous sale and profit data, you can draw a fitted representation curve that is used for profit prediction.
Sequential Patterns
This is a data mining technique that looks to identify similar patterns, trends, or regular events in transactional data over a business period.
In sales, establishing patterns can help businesses identify a set of items that customers buy together at different times. The businesses can then use the information to advise customers to buy it with better deals basing on their buying frequency.
Decision Trees
This is one of the most used data mining technique because its model is easily understandable for users. In this technique, the root of the decision is a simple question that has several answers. Each answer results to a set of conditions that help you determine the data that you can base your final decision on.
Data Mining Methods
There are several methods of data mining and data collection. Below, we discuss the most common methods of data mining and how they work.
i)Anomaly detection
This can be used to determine something is different from a regular pattern. For example, monitoring gas turbines and how you can detect anomaly to make sure the turbines are properly functioning. Sensors monitoring pressure and temperature are set up to see if anything abnormal is observed over time.
ii)Association learning
This is used to determine which things tend to occur simultaneously, either in pairs or groups. For example, it can be noted that people who buy milk often buy it break, and those that buy diapers, buy baby formula as well.
iii) Cluster detection
This is recognizing distinct groups within data, and the process is called cluster detection. Machine learning algorithms detect significantly worrying subgroups within a set of data.
iv) Classification
Unlike the case in cluster detection, classification deals with things that have labels. For example, spam filters are used to identify differences between content found in legit and spam messages. This can be done by identifying large sets of spam or email.
iv) Regression
This method is used to predict the future basing on the relationships within a data set. For example, the future engagement on the Facebook platform can be predicted based on everything in the user’s history like photo tags, likes, infractions with other users, friend requests, among others.
Similarly, another example would be the relationship between income and education level to predict the future of a neighborhood. The regression method allows all the aspects and relationships within a set of data to be analyzed and then used to tell the future behavior.
Data Mining Implementation Process
As you have learned earlier, data mining process the sorting through of large sets of data, relationships and insights that lead enterprises in measuring and managing where they performance is now and predicting where it will be in future.
Large sets of data are brought in from various data sources and may be kept in different data warehouses. Data mining techniques such as artificial intelligence, machine learning and predictive modelling can be involved.
The process of mining data requires commitment. Experts agree that the data mining implementation process is the same, and should follow a prescribed path.
Discussed below are the six essential steps in the data mining implementation process.
1. Business Understanding
In this phase you must fulfill a number of things. First, it is needed you understand your business objectives clearly and discovering what your business requires in order to prosper.Next, you have to asses the situation at hand by finding the assumptions, resources, constraints and other important factors that should be considered. Then, from your business objectives, build data mining goals to realize the business objectives within the current situation. Lastly, a credible data mining plan has to be implemented to achieve both data mining and business goals. You should ensure your plan is as detailed as possible.
2. Data Understanding
This is the second stage in the data mining implementation process.It starts with initial data collection which is sourced from available data sources to help get relevant with the data. Some essential activities must be done including data integration and data load in order to make the data collection a success. Then, the ‘surface’ properties of the sourced data needs to be examined very carefully and reported. Later, the data needs to be analysed by answering the data mining questions which can addressed through reporting ,quarrying and visualization. Finally, data quality must be analysed by answering essential questions like “Is the data acquired complete?”, “Are there any missing values in the acquired data?”
3. Data Preparation.
This process typically consumes about 90% of the period of the project. The outcome of data preparation stage is the data set. Once the available sources of data are identified, they need to be chosen, cleaned, constructed and formated into the ideal form. The task of data exploration at a greater depth maybe be done during this phase to identify the patterns based on business understanding.
4. Modelling
To begin with, modelling techniques have to chosen to be used for the prepared data set. Next, the test scene must be generated to determine the quality of the model. One or more models are then created on the data set prepared. Lastly, the models need to be assessed carefully involving all the stakeholders to ensure that created model meet the set business initiatives.
5. Evaluation
In this second last phase, the model results are evaluated in relation to business objectives drafted in the first stage, new business needs may be raised due to the new patterns discovered in the model results or from different factors. Understanding the business is a vital process in data mining. The do or don’t do decision must be made in this phase in order to move on the initiation phase.
6. Implementation
The information or knowledge gained through the entire data mining process needs to be showcased in a way that stakeholders can put it into use when they need it. Basing on the business requirements, the implementation phase could be as simple as creating a report or as complicated as repeating the entire data mining process across the company. In the implementation phase, the plans for deployment, monitoring and maintenance have to created for implementation and also for future support.
Data Mining Application in Business Analytics
Retail, finance, and marketing firms predominantly apply data mining in their operations. It helps create predictable vital information like target audiences, buying frequencies, and customer personality profiles. Therefore, data mining plays a crucial role in business analytics, as you will see in the next discussion.
As a decision-making tool
Competitor analysis, market research, and industry studies are key in the intelligent decision-making process of any company.
In the banking sector, customer data is analyzed to determine customer behavior. While the sector has statistical tools for more automated trend analysis, data mining applications have ensured structures move to more objective analysis.
A relationship management strategy is key for every company. For corporate organizations, CRM has to work towards improving company productivity and better client relations.
As Statistical and Quantitative Analysis
Statistical and quantitative marketing techniques have been applied mainly to business analytics. Database marketing has been pivotal for most companies and has been increasing since the evolution of the Internet. Businesses can now predict product marketing models, response, and potential target audience for any product before it even launches.
Data mining has also been applied in market basket analysis. This is a trend in the retail industry that has caught productivity with globalization. This helps design a specific strategy for the layout of the store and marketing strategy of various products and events.
Data mining provides financial institutions with vital information about loans and credit reporting by creating a model for historical customers. Additionally, it enables banks to detect fraudulent card transactions hence protecting the card’s owner.
In Marketing Campaigns
In marketing campaigns, data mining plays a key role. This is because it helps identify customer response. This enables businesses to know which products are on high demand and on those that aren’t.
Data mining provides customer response after a marketing campaign. This provides informational knowledge while determining customer groups.
Conclusion
In this article, you learned about the meaning of Data mining. You also learned about data mining concepts, data mining techniques, and data mining methods. We discussed how data mining is applied in business analytics as well as the data mining implementation process. We also briefly discussed the importance of data mining in machine learning.
