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Data Mining vs Machine Learning: Differences, Similarities & Use Examples

Oct 29, 202011 min read

Michał Rejman

IT Marketing Strategist at Ideamotive. Travel addict and remote work advocate.

Well, colleagues, it happened. It's time to admit it to ourselves. Artificial intelligence actively interacts almost everywhere. We mean not only in work processes but also in household chores.

 

Despite the mistrust of AI inspired by the film industry and futurists, it's time to breathe out and trust it. After all, most of the routine tasks can be easily crossed off from your to-do list right now. In particular, there are two main technologies worth paying attention to - data mining and machine learning.

 

Data mining and machine learning mainly focus on helping companies develop decision-making tools without much human intervention. Moreover, the decisions made can become the basis for action in one direction or another. 

 

Fear not, control is not lost. You yourself can set the limits of technology freedom. And this “freedom” is conditional as long as programs initially study your habits and develop decision-making algorithms that can predict your actions, direct you to potentially interesting development areas, or useful leads.

 

Hundreds of problems are resolved in a split second, thanks to the ability to perform an in-depth, comprehensive analysis of data that is usually stored chaotically and unstructured.

 

Sounds too good, right? Let's take a look at how each technology works separately, what are the differences and similarities between data mining vs machine learning, and which one would be the best solution for your business.

What is Data Mining?

The development of methods for recording and storing data has led to rapid growth in the amount of collected and analyzed information. The amount of data is so impressive that a person simply cannot analyze it on their own, although the need for such an analysis is quite obvious, because this "raw" data contains knowledge that can be used in decision-making. In order to conduct automatic data analysis, Data Mining is used.

 

Data Mining is the process of discovering previously unknown non-trivial, practically useful, and accessible interpretation of knowledge necessary for decision-making in various spheres of human activity in "raw" data. Data Mining is one of the steps of Knowledge Discovery in Databases.

 

The information found in the process of applying Data Mining methods must be non-trivial and previously unknown. For example, average sales do not fit this term. 

 

Knowledge should describe new relationships between properties, predict the values ​​of some features based on others, etc. The knowledge found should be applicable to new data with some degree of reliability. 

 

The usefulness lies in the fact that this knowledge can bring some benefit in its application. Knowledge should be in a non-mathematics understandable form for the user. 

 

For example, the logical constructions "if ... then ..." are most easily perceived by humans. Moreover, such rules can be used in various DBMS as SQL queries. In the case where the extracted knowledge is not transparent to the user, there should be post-processing methods to bring it to an interpretable form.

 

The algorithms used in Data Mining are computationally intensive. Previously, this was a limiting factor in the widespread practical application of Data Mining, but today's growth in the performance of modern processors has removed the acuteness of this problem. Now, in a reasonable time, you can conduct a qualitative analysis of hundreds of thousands and millions of records.

Tasks Solved by Data Mining Methods

  • Classification is the assignment of objects (observations, events) to one of the previously known classes.
  • Regression, including forecasting problems. Establishing the dependence of continuous output from input variables.
  • Clustering is a grouping of objects (observations, events) based on data (properties) that describe the essence of these objects. Objects within a cluster must be "similar" to each other and differ from objects included in other clusters. The more similar objects within a cluster, and the more differences between clusters, the more accurate the clustering.
  • Association - identifying patterns between related events. An example of such a pattern is the rule that indicates that event Y follows from event X. Such rules are called associative. This problem was first proposed to find typical shopping patterns in supermarkets. Therefore it is sometimes also called market basket analysis.
  • Sequential Patterns - establishing patterns between time-related events, i.e., detection of the dependence that if event X occurs, then event Y will occur after a specified time.
  • Deviation Analysis - identifying the most unusual patterns.

 

Business analysis problems are formulated in a different way, but the solution of most of them comes down to one or another Data Mining problem or a combination of them. 

 

For example, risk assessment is a solution to a regression or classification problem; market segmentation is clustering; demand stimulation is associative rules. 

 

In fact, Data Mining tasks are elements from which you can assemble a solution to the vast majority of real business problems.

 

To solve the above tasks, various Data Mining methods and algorithms are used. Due to the fact that Data Mining has developed at the intersection of disciplines such as statistics, information theory, machine learning, database theory, it is quite natural that most Data Mining algorithms and methods were developed based on various methods from these disciplines. 

 

For example, the k-means clustering procedure was simply borrowed from statistics. The following Data Mining methods have become very popular: 

  • neural networks, 
  • decision trees, 
  • clustering algorithms, including scalable ones, 
  • algorithms for detecting associative links between events, 
  • etc.

What is Machine Learning?

Machine learning (ML) is a set of methods in artificial intelligence, a set of algorithms that are used to create a machine that learns from experience. As training, the machine processes huge amounts of input data and finds patterns in them.

 

Machine learning is one of the branches of AI, algorithms that allow a computer to draw conclusions from data without following rigidly defined rules. That is, the machine can find patterns in complex and multi-parameter problems (which the human brain cannot solve), thus finding more accurate answers. The result is a correct prediction.

Machine Learning Purpose and Applications

The goal of machine learning is to partially or even completely automate the solution of various complex analytical problems.

 

Therefore, first of all, machine learning is designed to make the most accurate predictions based on input data so that business owners, marketers, and employees can make the right decisions in their work. As a result of training, the machine can predict the result, memorize it, reproduce it if necessary, and choose the best of several options.

 

At the moment, machine learning covers a wide range of applications from banks, restaurants, gas stations to robots in manufacturing. New challenges that arise almost daily lead to the emergence of new directions in machine learning.

How to Build Quality Machine Learning

Machine learning is built on three pillars:

  1. data - basic information that we usually ask the client to provide. This includes any data samples that need to be trained to work with the system;
  2. signs - this part of the work is carried out in close cooperation with the client. We identify key business needs and jointly decide which characteristics and properties the system should track as a result of training;
  3. algorithm - the choice of a method for solving a given business problem. We solve this problem without the participation of the client through the efforts of our employees.

Data

The more data we load into the system, the better and more accurate it will work. The data itself directly depends on the task that the machine faces.

 

For example, to teach mail to filter spam from important emails, examples are needed. And the larger the sample, the better. Thus, the system learns to perceive specific words - "Buy," "Additional income," "Earn at home," "Money," "Credit," "Potency increase" - as signs of spam and send such letters to a separate folder.

 

The initial data for other tasks will be different. To advise the buyer on products that may interest them, it needs a history of purchases made by the account holder. To predict price changes in the market, you need a price history.

 

The most difficult and, at the same time, voluminous part of the work is collecting this very data. There are two methods of data collection: 

  • manual. The manual method is much slower but accurate.
  • automatic. Automatic is much faster, but it allows for more errors.

 

A good sample of data is worth a lot because it is it who is responsible for the forecast accuracy that you get in the end. It is very important not to limit the collection of data to human thinking, but to provide as much information as possible, since the machine can see the benefits and relationships where the person will not notice them.

Signs (properties, metrics, features, characteristics)

For example, in the case of a car, the signs will be mileage, a number of cylinders, maximum possible speed. In the case of the buyer: age, gender, education, income level, etc. In the case of animals: breed, height, length from the tip of tail to nose, color.

 

Since the correctness of properties directly affects the result you get, their selection often takes longer than the machine learning process itself. The main thing here is not to limit the set of characteristics based on personal opinion, so as not to distort machine perception and the final result with it.

Algorithm

A system of sequential operations for solving a specific task. In other words, a solution method. For each specific task, you can choose a separate elegant algorithm. The speed and accuracy of the initial data processing result directly depend on the chosen method.

 

There are times when even perfectly written algorithms do not help to solve the set business problems. 

 

For example, if you want to increase the number of cross-selling on the site, and you are sure that for this, you just need to improve the product recommendation algorithm. But at the same time, you do not know that your customers are coming from direct links from search and ignore the advice for buying other products shown on the site. Therefore, before starting work, we determine the real cause of the client's problem. And if it is technical, AI is happy to help solve it.

Types of Machine Learning

Based on the presence of a teacher, training is divided into training with a teacher (Supervised Learning), without a teacher (Unsupervised Learning), and with reinforcement (Reinforcement Learning).

  • Supervised learning is used when you need to teach a machine to recognize objects or signals. The general principle of teaching with a teacher is “look, this is a door, and this is also a door, and this is a door too.”
  • Learning without a teacher uses the principle “this thing is the same as others.” Algorithms learn similarities and can detect differences and perform anomaly detection by recognizing what is unusual or dissimilar.
  • Reinforcement learning is used where the machine is faced with the task of correctly performing the assigned tasks in the external environment with many possible options for action. For example, in computer games, trading operations for unmanned vehicles.

By the type of algorithms used, two types can be distinguished:

  • classical learning - well-known and well-studied learning algorithms, mainly developed more than 50 years ago for statistical offices. Suitable primarily for tasks of working with data: classification, clustering, regression, etc. They are used for forecasting, customer segmentation, and so on.
  • neural networks and deep learning - the most modern approach to machine learning. Neural networks are used wherever image and video recognition or generation, complex control algorithms or decision-making, machine translation, and similar complex tasks are needed.

Several approaches can be combined, and then you get ensembles of models.

Data Mining and Machine Learning - similarities

Here is the list of similarities you can find while comparing data mining vs machine learning:

  • These are all buzzwords that have caught the attention of the media since tech giants like Google and Facebook started using them at the end of the last decade.
  • All of them are somehow related to the extraction of information for a specific purpose.
  • They deal with mathematics, algorithms, and statistics.
  • They all use algorithmic approaches to sift through data, different tools, and applications.
  • In some cases, they use similar algorithmic or structural approaches.

Data Mining and Machine Learning - differences

Key differences between data mining vs machine learning technologies:

  • Data mining functionality is strictly limited to collecting information from various resources. The technology itself does not make decisions. It is not capable of doing any actions without your intervention. Its aim is to find useful ways to use the data that has been found. It distinguishes between sets to retrieve data and compute data from them, what goes into segments, and what does not. This technology can be used to differentiate data and obtain datasets for partitions.
  • Machine learning works with data arrays generated by data mining technology. Using pre-modeled algorithms for actions, AI technology uses data to make decisions and follow up. This technology does not exist without a permanent backup of up-to-date information.

Finally, we get our ecosystem of making informed decisions. Both technologies complement each other. Using them alone is to limit their potential.

Examples of Data Mining solutions for business 

Fed up with theory? Let’s see what are the best data mining uses in business.

Banking

With the help of Data Mining tools, it is possible to classify into "more profitable" and "less profitable" clients. After determining the most profitable segment of customers, it makes sense for the bank to pursue a more active marketing policy to attract customers precisely among the group found.

Retail 

Retail uses DM to analyze the target audience. Potential customers can be found by focusing on the basic characteristics that unite the existing user base. Also, data mining helps to analyze the effectiveness of a brand's service or product and decide on the necessary improvements. Targeting potentially interested consumers is also possible with this technology.

eCommerce

In the field of e-commerce, Data Mining is used to form recommendation systems and solve problems of classifying website visitors. This classification allows companies to identify specific customer groups and conduct marketing policies in line with the identified customer interests and needs. Data Mining technology for e-commerce is closely related to Web Mining technology.

 

E-commerce is thriving precisely because of a deep analysis of the user's previous history of activity. Your user recently read about the top 10 vacation spots this summer, can you offer a great deal from your travel agency? We do not waste time. We establish connections with the client!

Insurance

The insurance business is associated with a certain risk. Here, the tasks solved using Data Mining are similar to those in banking.

 

The information obtained by segmenting customers into groups is used to define client groups. As a result, the insurance company can, with the greatest benefit and the least risk, offer certain groups of services to specific groups of customers.

 

The task of detecting fraud is solved by finding a certain common stereotype of the behavior of fraudulent clients.

Telecommunications

In telecommunications, Data Mining's advances can be used to solve a problem typical of any company that works to attract loyal customers - determining the loyalty of those customers. 

 

The need to solve such problems is due to tough competition in the telecommunications market and the constant migration of customers from one company to another. As you know, retaining a client is much cheaper than returning it. Therefore, it becomes necessary to identify certain groups of customers and develop a set of services that are most attractive to them. In this area, as in many others, an important task is to identify the facts of fraud.

 

In addition to such tasks, which are typical for many areas of activity, there is a group of tasks determined by the specifics of the telecommunications sector.

Industrial production

The peculiarities of industrial production and technological processes create good prerequisites for the possibility of using Data Mining technology in the course of solving various production problems. The technical process by its nature should be controlled, and all its deviations are within the previously known limits. Here we can talk about certain stability, which is usually not inherent in most of the tasks facing Data Mining technology.

 

The main tasks of Data Mining in industrial production:

  • comprehensive system analysis of production situations;
  • short-term and long-term forecast of the development of production situations;
  • development of options for optimization solutions;
  • forecasting the quality of the product depending on some parameters of the technological process;
  • detection of hidden trends and patterns of development of production processes;
  • forecasting patterns of development of production processes;
  • detection of hidden factors of influence;
  • detection and identification of previously unknown relationships between production parameters and influencing factors;
  • analysis of the environment of the interaction of production processes and forecasting changes in its characteristics;
  • development of optimization recommendations for managing production processes;
  • visualization of analysis results, preparation of preliminary reports, and drafts of feasible solutions with assessments of the reliability and effectiveness of possible implementations.

Real-life examples

Examples of Machine Learning solutions for business

Feel that ML is exactly what your project needs? Check out the most interesting machine learning use cases.

Selling systems for online stores

A decent business example is recommendation systems for online stores. The task is clear: based on the analysis of user behavior on the site and their purchases, offer them additional products that the client is likely to buy. 

 

A training program is created, an algorithm that analyzes a huge amount of data on purchases in various online stores and, after training, can produce fairly accurate predictions for new customers. 

 

Practice shows that a good recommendation system can ensure an online store's revenue growth of up to 50%.

Customer behavior predictions

Machine learning techniques are often used to predict events in the customer base. An insurance company providing VHI services can use machine learning to predict which clients will seek expensive medical care in the near future. With such a forecast, the company contacts “high-risk” clients in advance and takes preventive measures: for example, it offers the client a medical examination or arranges a consultation with a more qualified doctor. 

 

Clients began to receive qualified assistance in advance, without waiting for the acute phase of the illness, and the insurance company reduces VHI costs by hundreds of thousands of dollars a month!

Process optimization

Machine learning allows you to optimally organize the supply of goods to retail chains. One of the network retailers recently implemented a self-learning system that completely independently forms an order for the supply of drinking water to the chain's stores. The program takes into account the dynamics of sales, weather forecast, season, and other factors, allows you to avoid overstocking or, conversely, a shortage of goods at the point of sale.

Segmentation when working with clients

With the help of training programs, the tasks of not only forecasting but also segmentation are effectively solved. 

 

The brightest example is finding clients that are similar to a certain group. A network of dental clinics with several tens of thousands of entries in the client base has implemented such a case

 

They took customers who had recently ordered a professional hygiene service, and based on this data, using machine learning methods, they identified the most likely buyers of this service from their entire customer base. Such clever segmentation can reduce the cost of soulless calling several times!

Real-life cases:

Summary

Read the whole article but didn’t come up with the decision on what is better: data mining vs machine learning?

 

Here is a brief retelling:

Wondering if Data Mining or Machine Learning is for you? Want to know how you can benefit? Reach out to us, talk with our ML/AI advisors, data engineers, software consultants, and data scientists to learn how you can boost your business.

Michał Rejman

Michał is a digital marketing veteran with a growth hacking mindset and 10+ years of experience. His goal is building high-quality technological content, with particular emphasis on React and Ruby on Rails. Traveler, climber, remote work advocate.

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