Our modern information age leads to a dynamic and extremely high growth of the data mining world. No doubt, that it requires adequate and effective different types of data analysis methods, techniques, and tools that can respond to constantly increasing business research needs.
In fact, data mining does not have its own methods of data analysis. It uses the methodologies and techniques of other related areas of science.
Among the methods used in small and big data analysis are:
- Mathematical and statistical techniques
- Methods based on artificial intelligence, machine learning
- Visualization and graphical method and tools
Here we will see a list of the most known classic and modern types of data analysis methods and models.
Mathematical and Statistical Methods for Data Analysis
Mathematical and statistical sciences have much to give to data mining management and analysis. In fact, most data mining techniques are statistical data analysis tools. Some of methods and techniques are well known and very effective.
1. Descriptive Analysis
Descriptive analysis is an insight into the past. This statistical technique does exactly what the name suggests -“Describe”. It looks at data and analyzes past events and situations for getting an idea of how to approach the future.
It allows us to learn from past behaviors, and find out how they might influence future performance.
2. Regression Analysis
Regression analysis allows modeling the relationship between a dependent variable and one or more independent variables. In data mining, this technique is used to predict the values, given a particular dataset. For example, regression might be used to predict the price of a product, when taking into consideration other variables.
Regression is one of the most popular types of data analysis methods used across a variety of industries for business or marketing planning, financial forecasting, and more.
There is a huge range of different types of regression models such as linear regression models, multiple regression, logistic regression, ridge regression, nonlinear regression, life data regression, and many many others.
3. Factor Analysis
Factor analysis is a regression based data analysis technique, used to find an underlying structure in a set of variables.
It goes with finding new independent factors (variables) that describe the patterns and models of relationships among original dependent variables.
Factor analysis is a very popular tool for researching variable relationships for complex topics such as psychological scales and socioeconomic status.
FA is a basic step towards effective clustering and classification procedures.
4. Dispersion Analysis
Dispersion analysis in not a so common method used in data mining but still has a role there. Dispersion is the spread to which a set of data is stretched. It is a technique of describing how extended a set of data is.
Generally, the dispersion has two matters: first it represents the variation of the things among themselves, and second, it represents the variation around the average value. If the difference between the value and average is significant, then dispersion is high. Otherwise, it is low.
5. Discriminant Analysis
Discriminant analysis is one of the most powerful classification techniques in data mining. The discriminant analysis utilizes variable measurements on different groups of items to underline points that distinguish the groups.
These measurements are used to classify new items.
Typical examples of this method uses are: in classifying applications for credit cards into low risk and high-risk categories, classifying customers of new products into different groups, medical studies implicating alcoholics and non-alcoholics, and etc.
6. Time Series Analysis
You know that, in almost every scientific area, measurements are executed over time. These look-outs lead to a collection of organized data known as time series.
A good example of time series is the daily value of a stock market index.
Time series data analysis is the process of modeling and explaining time-dependent series of data points. The goal is to draw all meaningful information (statistics, rules, and patterns) from the shape of data.
Afterward, this information is used for creating and modeling forecasts that are able to predict future evolutions.
Methods Based on The Artificial Intelligence, Machine Learning and Heuristic Algorithms
These modern methods attract the attention of data scientists with their extended capabilities and the ability to solve non-traditional tasks. In addition, they can be easily and efficiently implemented and performed by special software systems and tools.
Here is a list of some of the most popular of these types of data analysis methods:
7. Artificial Neural Networks
No doubt that this is one of the most popular new and modern types of data analysis methods out there.
According to http://neuralnetworksanddeeplearning.com ,”Neutral Networks are a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data”
Artificial Neural Networks (ANN), often just called a “neural network”, present a brain metaphor for information processing.
These models are biologically inspired computational models. They consist of an interconnected group of artificial neurons and process information using computation approach.
The application of neural networks in the data mining is very broad. They have high acceptance ability for noisy data and high accuracy. The data mining based on neural networks is researched in detail. Neural networks have been shown to be very promising systems in many forecasting and business classification applications.
8. Decision Trees
This is another very popular and modern classification algorithm in data mining and machine learning. The decision tree is a tree-shaped diagram that represents classification or regression models.
It divides a data set into smaller and smaller sub datasets (that contain instances with similar values) while at the same time a related decision tree is continuously developed. The tree is built to show how and why one choice might lead to the next, with the help of the branches.
Among the benefits of using decision trees are: domain knowledge is not required; they are easy to comprehend; the classification steps of a decision tree are very simple and fast.
9. Evolutionary Programming
Evolutionary programming in data mining is a common concept that combines many different types of data analysis using evolutionary algorithms. Most popular of them are: genetic algorithms, genetic programming, and co-evolutionary algorithms.
In fact, many data management agencies apply evolutionary algorithms to deal with some of the world’s biggest big-data challenges.
10. Fuzzy Logic
Fuzzy logic is applied to cope with the uncertainty in data mining problems. Fuzzy logic modeling is one of the probability based data analysis methods and techniques.
It is a relatively new field but has a great potential for extracting valuable information from different data sets.
Fuzzy logic is an innovative type of many-valued logic in which the truth values of variables are a real number between 0 and 1. In this term, the truth value can range between completely true and completely false.
Fuzzy logic is applicable when the model contains parameters whose values can not be precisely determined or these values contain too high a level of noise.
The types of data analysis methods are just a part of the whole data management picture that also includes data architecture and modeling, data collection tools, warehousing, data security, data quality metrics and management, data mapping and integration, business intelligence, and etc.
What type of data analysis to use? No single data analysis method or technique can be defined as the best technique for data mining. All of them has their role, meaning, advantages and disadvantages.
The selection of methods depends on the particular problem and your data set. Data may be your most valuable tool. So, choosing the right methods of data analysis might be a crucial point for your overall business development.
Silvia Vylcheva has more than 10 years of experience in the digital marketing world – which gave her a wide business acumen and the ability to identify and understand different customer needs.
Silvia has a passion and knowledge in different business and marketing areas such as inbound methodology, data intelligence, competition research and more.