Data mining is the process of understanding data through cleaning raw data, finding patterns, creating models, and testing those models. It includes statistics, machine learning, and database systems. Data mining often includes multiple data projects, so it’s easy to confuse it with analytics, data governance, and other data processes. This guide will define data mining, share its benefits and challenges, and review how data mining worksInquiry Online
Data mining has a long history. It emerged with computing in the 1960s through the 1980s. Historically, data mining was an intensive manual coding process — and it still involves coding ability and knowledgeable specialists to clean, process, and interpret data mining results today. Data specialists need statistical knowledge and some programming language knowledge to complete data mining techniques accurately. For instance, here are some examples of how companies have used R to answer their data questions. However, some of the manual processes are now able to be automated with repeatable flows, machine learning (ML), and artificial intelligence (AI) systems.
As discussed, data mining may be confused with other data projects. The data mining process includes projects such as data cleaning and exploratory analysis, but it is not just those practices. Data mining specialists clean and prepare the data, create models, test those models against hypotheses, and publish those models for analytics or business intelligence projects. In other words, analytics and data cleaning are parts of data mining, but they are only parts of the whole.
Data mining is most effective when deployed strategically to serve a business goal, answer business or research questions, or be a part of a solution to a problem. Data mining assists with making accurate predictions, recognizing patterns and outliers, and often informs forecasting. Further, data mining helps organizations identify gaps and errors in processes, like bottlenecks in supply chains or improper data entry.
The first step in data mining is almost always data collection. Today’s organizations can collect records, logs, website visitors’ data, application data, sales data, and more every day. Collecting and mapping data is a good first step in understanding the limits of what can be done with and asked of the data in question
The Cross-Industry Standard Process for Data Mining (CRISP-DM) is an excellent guideline for starting the data mining process. This standard was created decades ago and is still a popular paradigm for organizations that are just starting.
The CRISP-DM comprises a six-phase workflow. It was designed to be flexible; data teams are allowed and encouraged to move back to a previous stage if needed. The model also provides opportunities for software platforms that help perform or augment some of these tasks.
Once the business problem is understood, it is time to collect the data relevant to the question and get a feel for the data set. This data often comes from multiple sources, including structured data and unstructured data. This stage may include some exploratory analysis to uncover some preliminary patterns. At the end of this phase, the data mining team has selected the subset of data for analysis and modeling.
This phase begins with more intensive work. Data preparation involves preparing the final data set, which includes all the relevant data needed to answer the business question. Stakeholders will identify the dimensions and variables to explore and prepare the final data set for model creation.
In this phase, you’ll select the appropriate modeling techniques for the given data. These techniques can include clustering, predictive models, classification, estimation, or a combination. Front Health used statistical modeling and predictive analytics to decide whether to expand healthcare programs to other populations. You may have to return to the data preparation phase if you select a modeling technique that requires selecting other variables or preparing some different sources.
After creating the models, you need to test them and measure their success at answering the question identified in the first phase. The model may answer facets of things not accounted for, and you may need to edit the model or edit the question. This phase is designed to allow you to look at the progress so far and ensure it’s on the right track for meeting the business goals. If it’s not, there might be a need to move backwards to previous steps before a project is ready for the deployment phase.
Finally, once the model is accurate and reliable, it is time to deploy it in the real world. The deployment can take place within the organization, be shared with customers, or be used to generate a report for stakeholders to prove its reliability. The work doesn’t end when the last line of code is complete; deployment requires careful thought, a roll-out plan, and a way to make sure the right people are appropriately informed. The data mining team is responsible for the audience’s understanding of the project.
Data mining includes multiple techniques for answering the business question or helping solve a problem. This section is just an introduction to two data mining techniques and is not currently comprehensive.
The most common technique is classification. To do this, identify a target variable and then divide that variable into appropriate level of detail categories. For example, the variable ‘occupation level’ might be split into ‘entry-level’, ‘associate’, and ‘senior’. With other fields such as age and education level, you can train your data model to predict what occupation level a person is more likely to have. You may add an entry for a recent 22-year-old graduate, and the data model could automatically classify that person in an ‘entry-level’ position. Insurance or financial institutions such as PEMCO Insurance used classification to train their algorithms to flag fraud and to monitor claims.
Clustering is another common technique, grouping records, observations, or cases by similarity. There won’t be a target variable like in classification. Instead, clustering just means separating the data set into subgroups. This method can include grouping records of users by geographic area or age group. Typically, clustering the data into subgroups is preparation for analysis. The subgroups become inputs for a different technique.
Data mining is a powerful and useful process for exploring data to predict patterns or outcomes. Unfortunately, it’s easy to do data mining incorrectly. You shouldn’t use data mining if your leaders do not have analytical or statistical knowledge to oversee the software. Inaccurate mining techniques can create incorrect models, resulting in inaccuracies. Further, if the team is using personally identifiable information in data mining activities, they must ensure they are following compliance regulations and governance standards.
Data mining specialization is most often a function or capability of data scientist or data analyst roles. Data mining tends to require large projects with far-reaching, cross-functional project management, and it can ladder up to analytics or business analysis teams. Some organizations look to data mining specialists to build machine learning or artificial intelligence scripts, so proficiency and knowledge of these is often a core competency. Within research organizations or in academia, data mining specialists are likely to be called data scientists or analysts and they can exist either as a part of a single lab or as a part of a service center or center of excellence team for many labs.
Our customers, partners, and researchers have used data mining and R to innovate and maximize productivity. For example, Wells Fargo needed to clean up user data from 70 million customers to gain clear insights. Their data team was able to use Tableau and R to maximize their computing power and complete major projects much faster than with traditional tools. Modern platforms empower users to get deep into data mining without overwhelming data teams.
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Data mining is the process of examining vast quantities of data in order to make a statistically likely prediction. Data mining could be used, for instance, to identify when high spending customers interact with your business, to determine which promotions succeed, or explore the impact of the weather on your business
Data analytics and the growth in both structured and unstructured data has also prompted data mining techniques to change, since companies are now dealing with larger data sets with more varied content. Additionally, artificial intelligence and machine learning are automating the process of data mining
Data mining is an highly effective process – with the right technique. The challenge is choosing the best technique for your situation, because there are many to choose from and some are better suited to different kinds of data than others. So what are the major techniques?
This form of analysis is used to classify different data in different classes. Classification is similar to clustering in that it also segments data records into different segments called classes. In classification, the structure or identity of the data is known. A popular example is e-mail to label email as legitimate or as spam, based on known patterns
The opposite of classification, clustering is a form of analysis with the structure of the data is discovered as it is processed by being compared to similar data. It deals more with the unknown, unlike classification
A statistical process for estimating the relationships between variables which helps you understand the characteristic value of the dependent variable changes. Generally used for predictions, it helps to determine if any one of the independent variables is varied, so if you change one variable, a separate variable is affected
This technique is what data mining is all about. It uses past data to predict future actions or behaviors. The simplest example is examining a person’s credit history to make a loan decision. Induction is similar in that it asks if a given action occurs, then another and another again, then we can expect this result
One of the many forms of data mining, sequential patterns are specifically designed to discover a sequential series of events. It is one of the more common forms of mining as data by default is recorded sequentially, such as sales patterns over the course of a day
Decision tree learning is part of a predictive model where decisions are made based on steps or observations. It predicts the value of a variable based on several inputs. It’s basically an overcharged “If-Then” statement, making decisions on the answers it gets to the question it asks
This is one of the most basic techniques in data mining. You simply learn to recognize patterns in your data sets, such as regular increases and decreases in foot traffic during the day or week or when certain products tend to sell more often, such as beer on a football weekend
While most data mining techniques focus on prediction based on past data, statistics focuses on probabilistic models, specifically inference. In short, it’s much more of an educated guess. Statistics is only about quantifying data, whereas data mining builds models to detect patterns in data
Data visualization is the process of conveying information that has been processed in a simple to understand visual form, such as charts, graphs, digital images, and animation. There are a number of visualization tools, starting with Microsoft Excel but also RapidMiner, WEKA, the R programming language, and Orange
Neural network data mining is the process of gathering and extracting data by recognizing existing patterns in a database using an artificial neural network. An artificial neural network is structured like the neural network in humans, where neurons are the conduits for the five senses. An artificial neural network acts as a conduit for input but is a complex mathematical equation that processes data rather than feels sensory input
You can’t have data mining without data warehousing. Data warehouses are the databases where structured data resides and is processed and prepared for mining. It does the task of sorting data, classifying it, discarding unusable data and setting up metadata
This is a method to identify interesting relations and interdependencies between different variables in large databases. This technique can help you find hidden patterns in the data that that might not otherwise be clear or obvious. It’s often used in machine learning
Data processing tends to be immediate and the results are often used, stored, or discarded, with new results generated at a later date. In some cases, though, things like decision trees are not built with a single pass of the data but over time, as new data comes in, and the tree is populated and expanded. So long-term processing is done as data is added to existing models and the model expands
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