DATA MINING DEFINITION

DATA MINING DEFINITION:-Data mining is the process of finding anomalies, patterns and correlations within large data sets to predict outcomes. Using a broad range of techniques, you can use this information to increase revenues, cut costs, improve customer relationships, reduce risks and more.

Data mining is the process of analyzing dense volumes of data to find patterns, discover trends, and gain insight into how that data can be used. Data miners can then use those findings to make decisions or predict an outcome. Data mining is an interconnected discipline, blending the fields of statistics, machine learning, and artificial intelligence.

In General, data mining (DATA MINING DEFINITION)is nothing but a process of finding or extracting useful information from huge volumes of data. You may get familiar if we use the term big data. Although using a big range of techniques can help us to use this information to increase revenues, cost-cutting and improve customer relationships, etc. It may be quite possible that you may be thinking that is why data mining is so important. The answer to this question is quite complex. However, it is not the answer that is actually big. You may have seen staggering numbers; the volumes of produced data are getting doubled every two years. However, this growth rate of the data is also increasing, or it will be correct to say that data is getting doubled even in less than two years.

A data warehouse is where data can be collected for mining purposes, usually with large storage capacity. Various organizations’ systems are in the data warehouse, where it can be fetched as per usage.

Data warehouses collaborate data from several sources and ensure data accuracy, quality, and consistency. System execution is boosted by differentiating the process of analytics from traditional databases. In a data warehouse, data is sorted into a formatted pattern by type and as needed. The data is examined by query tools using several patterns.

Data warehouses store historical data and handle requests faster, helping in online analytical processing, whereas a database is used to store current transactions in a business process that is called online transaction processing.

Data mining is vital to business operations across many industries. Companies use data mining to manage risk, anticipate demands for resources, project customer sales, detect fraud, and increase response rates to their marketing efforts.

This is a six-step procedure for turning data into insight. The model works like this:

DATA MINING DEFINITION

Business Understanding

This is the starting point. What questions do you have? What do you want to learn from your data? Companies and organizations first must identify their objectives, including what insights they want to extract or problems they want to solve using their collected data. Determining project goals is important for collecting the right data to be analyzed.

Data Understanding

Once the objective is defined, it’s time to define the data. Not every data point stored on a server or in the cloud is appropriate for every project. Determining the right data to be sourced saves time and the potential hassle of retracing steps later.

In this phase, data is collected from multiple sources based on the problem being addressed. Is the company looking for historical sales of a certain item? The type of credit card used to make a purchase? Whether items were bought in store or online? Each type of data may be relevant — or not — depending on the project.

This part of the process is important for verifying data quality as well. Missing, errant, or duplicate data can be corrected before moving to the next phase.

Data Preparation

Data preparation is considered the most demanding phase of data mining, often consuming at least half of the project’s time and effort. It’s in this step that the most helpful data is selected, cleaned, and sorted to account for errors or coding inconsistencies. Data from multiple sources can be merged, organized, or adjusted in different ways to prepare for the next phase: modeling.

Modeling

Now the data begins to take shape. Data miners can run a variety of models (ways of organizing data) to generate solutions. For instance, models can seek to detect patterns or anomalies in the data or use the data to predict an outcome. Companies will choose the model based on the type of data they’re analyzing, the project’s specific requirements, and the goals being pursued.

Several modeling techniques can be used on the same set of data to derive different results. Rarely do companies answer their data mining question with just one model.

Evaluation

At this point, data miners assess whether the models have produced a satisfactory answer to the question asked and whether the results contain any unexpected or unique findings.

If the initial question remains unanswered, a new model might be required, or the data might need to be changed. If the results meet their criteria, the project moves to its final phase.

Deployment

At this point, companies have answered the question they asked. In the flower shop example, perhaps the model suggested an increased order due to past sales and expected customer demand. The florist can deploy that knowledge to ensure they have enough flowers on hand when a major event arrives.

DATA MINING DEFINITION: Features of Data mining

These are the following key features that data mining usually allows us:

  • Sift through all the chaotic and repetitive noise in your data.
  • Allows understanding what is relevant and then making good use of that information to assess likely outcomes.
  • Accelerate the pace of making informed decisions.

DATA MINING DEFINITION: Why do we need Data Mining?

In today’s modern world, we are all surrounded by big data, which is predicted to be grown by 40% by the next decade. You may wonder that the real fact is that we are drowning in the data, but at the same time, we are starving for knowledge (or useful Data). The main reason behind this, all this data creates noise which makes it difficult to mine. In short, we have generated tons of amorphous data but experiencing failing big data initiatives as the useful data is deeply buried inside. Therefore, without powerful tools such as Data Mining, we cannot mine such data, and as a result, we will not get any benefits from that data.

DATA MINING DEFINITION: Types of Data Mining

Each of the following data mining techniques serves several different business problems and provides a different insight into each of them. However, understanding the type of business problem you need to solve will also help in knowing which technique will be best to use, which will yield the best results. The Data Mining types can be divided into two basic parts that are as follows:

  1. Predictive Data Mining Analysis
  2. Descriptive Data Mining Analysis

1. Predictive Data Mining

As the name signifies, Predictive Data-Mining analysis works on the data that may help to know what may happen later (or in the future) in business. Predictive Data-Mining can also be further divided into four types that are listed below:

  • Classification Analysis
  • Regression Analysis
  • Time Serious Analysis
  • Prediction Analysis

2. Descriptive Data Mining

The main goal of the Descriptive Data Mining tasks is to summarize or turn given data into relevant information. The Descriptive Data-Mining Tasks can also be further divided into four types that are as follows:

  • Clustering Analysis
  • Summarization Analysis
  • Association Rules Analysis
  • Sequence Discovery Analysis

Here, we will discuss each of the data mining’s types in detail. Below are several different data mining techniques that can help you find optimal outcomes as the results.

1. CLASSIFICATION ANALYSIS

This type of data mining technique is generally used in fetching or retrieving important and relevant information about the data & metadata. It is also even used to categories the different types of data format into different classes. If you focus on this article until it ends, you may definitely find out that Classification and clustering are similar data mining types. As clustering also categorizes or classify the data segments into the different data records known as the classes. However, unlike clustering, the data analyst would have the knowledge of different classes or clusters. Therefore, in the classification analysis, you have to apply or implement the algorithms to decide in which way the new data should be categorized or classified. A classic example of classification analysis would be Outlook email. In Outlook, they use certain algorithms to characterize an email is legitimate or spam.

This technique is usually very helpful for retailers who can use it to study the buying habits of their different customers. Retailers can also study the past sales data and then lookout (or search) for products that customers usually buy together. After which, they can put those products nearby of each other in their retail stores to help customers save their time and as well as to increase their sales.

2. REGRESSION ANALYSIS

In statistical terms, regression analysis is a process usually used to identify and analyze the relationship among variables. It means one variable is dependent on another, but it is not vice versa. It is generally used for prediction and forecasting purposes. It can also help you understand the characteristic value of the dependent variable changes if any of the independent variables is varied.

3. Time Serious Analysis

A time series is a sequence of data points that are usually recorded at specific time intervals of points. Usually, they are – most often in regular time intervals (seconds, hours, days, months etc.). Almost every organization generates a high volume of data every day, such as sales figures, revenue, traffic, or operating cost. Time series data mining can help in generating valuable information for long-term business decisions, yet they are underutilized in most organizations.

4. Prediction Analysis

This technique is generally used to predict the relationship that exists between both the independent and dependent variables as well as the independent variables alone. It can also use to predict profit that can be achieved in future depending on the sale. Let us imagine that profit and sale are dependent and independent variables, respectively. Now, on the basis of what the past sales data says, we can make a profit prediction of the future using a regression curve.

5. Clustering Analysis

In Data Mining, this technique is used to create meaningful object clusters that contain the same characteristics. Usually, most people get confused with Classification, but they won’t have any issues if they properly understand how both these techniques actually work. Unlike Classification that collects the objects into predefined classes, clustering stores objects in classes that are defined by it. To understand it in more detail, you can consider the following given example:

Example

Suppose you are in a library that is full of books on different topics. Now the real challenge for you is to organize those books so that readers don’t face any problem finding out books on any particular topic. So here, we can use clustering to keep books with similarities in one particular shelf and then give those shelves a meaningful name or class. Therefore, whenever a reader looking for books on a particular topic can go straight to that shelf. Hence, he won’t be required to roam the entire library to find the book he wants to read.

6. SUMMARIZATION ANALYSIS

The Summarization analysis is used to store a group (or a set) of data in a more compact way and an easier-to-understand form. We can easily understand it with the help of an example:

Example

You might have used Summarization to create graphs or calculate averages from a given set (or group) of data. This is one of the most familiar and accessible forms of data mining.

7. ASSOCIATION RULE LEARNING

In general, it can be considered a method that can help us identify some interesting relations (dependency modeling) between different variables in large databases. This technique can also help us to unpack some hidden patterns in the data, which can be used to identify the variables within the data. It also helps in detecting the concurrence of different variables that appear very frequently in the dataset. Association rules are generally used for examining and forecasting the behavior of the customer. It is also highly recommended in the retail industry analysis. This technique is also used to determine shopping basket data analysis, catalogue design, product clustering, and store layout. In IT, programmers also uses the association rules to create programs capable of machine learning. Or in short, we can say that this data mining technique helps to find the association between two or more Items. It discovers a hidden pattern in the data set.

8. Sequence Discovery Analysis

The primary goal of sequence discovery analysis is to discover interesting patterns in data on the basis of some subjective or objective measure of how interesting it is. Usually, this task involves discovering frequent sequential patterns with respect to a frequency support measure. Some people may often confuse it with time series as both the Sequence discovery analysis and Time series analysis contains the adjacent observation that are order dependent. However, if the people see both of them in a little more depth, their confusion can be easily avoided as the Time series analysis technique contains numerical data, whereas the Sequence discovery analysis contains discrete values or data.

DATA MINING DEFINITION:USES OF DATA MINING

Healthcare

Data mining has been embedded in healthcare for years. Physicians take advantage of more effective treatment methods based on data mined from clinical trials and patient studies. Hospitals and clinics can improve patient outcomes and safety while cutting costs and lowering response times. Data mining can even match patients with doctors based on reports of successful diagnosis rates.

Banking and Finance

Among the first uses of data mining was the detection of credit card fraud. Financial companies also mine their billions of transactions to measure how customers save and invest money, allowing them to offer new services and constantly test for risk.

Retail

Retailers have an enormous amount of customer data (purchase trends, preferences, and spending habits among them) that they attempt to leverage to boost future sales. Retail companies that don’t produce insight from data mining risk falling behind the competition.

Insurance

Fraud detection is a critical component of the insurance industry, but insurers also use data to manage risk, understand why they’re losing customers, and price their products more effectively. For instance, a car insurance company could study mileage and accident rates for a certain region to determine whether it should raise or lower rates for customers who live there.

Media and Telecommunications

Media and telecommunications companies have loads of data on consumer preferences, including the programming they watch, books they read, and video games they play. With that data, companies can target programming to consumers by taste, region, or other factors. They can even suggest media to consume — an approach companies like Netflix have mastered.

Education

By measuring student achievement data, educators believe they can predict when students might drop out of school before the students even consider it. Further, this data can help educators intervene with at-risk students and potentially keep them in school.

Manufacturing

Manufacturers use data to align their production schedules with demand, ensuring that products are on store (or virtual) shelves when they’re needed. This helps maximize production at critical times and predict when assembly lines might need maintenance.

Transportation

Safety is a primary driver of data mining in the transportation industry. Cities and communities can conduct traffic studies to determine the busiest roads and intersections. And public transportation entities can mine data to understand their busiest zones and travel times.