Data mining techniques

The most important task of data mining is the selection of appropriate data mining techniques. These are chosen based on the business type and the issue they are to address. The techniques range from general to more specific ones. If unfamiliar with the term, data mining is the process of extracting usable data and patterns and using them to predict future trends.

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Data wrangling and data mining

Data wrangling (also: data munging) is the process of clearing raw data and transforming them into a format suitable for analysis. Data wrangling solutions are designed with the purpose of allowing the user to explore data for downstream uses.

Data mining: How it works

Data mining involves three steps: exploration, pattern identification and deployment.

Exploration is the process that sees data cleared and converted into another format. Pattern identification creates the most suitable pattern which is capable of the best predictions. Deployment sees the patterns being used to achieve the desired outcome.

Some of the benefits of data mining include automated prediction of future trends, speed of analysis, variety of models, automated hidden pattern discovery, overall implementation (e.g., data mining tools fully implement with existing platforms and new ones alike), etc.

Minerra offers some of the finest automated BI and analysis tools that will help you to always make data-driven decisions. We also offer personalised training and BI consultancy. Give us a call to see for yourself how automated data mining can benefit your business in the long run.

Data mapping

Before we dive deeper into the mining techniques, it is necessary to explain data mapping. It is the process that sources data fields to their related target data fields. These comprise metadata storing information on the individual data parts, fields, attributes, objects, etc. As such, data mapping is critical in helping decision makers make optimum business choices. Coupled with data mining, it is one of the most important aspects of BI.

Types of data mining techniques

Generally speaking, there are seven main data mining techniques. Note the term “main.” Certainly there are additional ones, but these seven are the most commonly used ones. They include: statistics, clustering, visualisation, decision tree, neural networks, association rules and classification.

• Statistics

Statistics deals with data collection and description. It helps with pattern discovery and predictive models. Statistics answers general questions, such as “what is the probability of certain event occurring?” or “which pattern is the most useful one to the business?” Statistical reports collect data through varied approaches, most common of which are median, variance, min, max, histogram and linear regression.

• Clustering

Clustering is among the oldest data mining techniques. Clustering analysis identifies similar data and attempts to comprehend the similarities and differences between them. This process is also known as “segmentation.” Similarly to statistics, clustering uses a number of approaches, best known of which are model-based methods, partitioning methods, grid-based methods, density-based methods and hierarchical agglomerative methods.

The most famous clustering algorithm is called the “Nearest Neighbour”. It gives the answer to the so-called “travelling salesman problem” by answering the question: “given a list of cities and the distances between each pair of cities, what is the shortest possible route that visits each city and returns to the origin city?” Applying the answer to data, the algorithm predicts what the estimated value of one record looks like for similar estimated values.

The main thesis is that objects close to one another have similar prediction values. The Nearest Neighbour technique is most commonly used for text retrieval, where it finds the documents that share the same attributes as the main one. Clustering is an integral part of all analytics tools.

• Visualisation

Visualisation is used for discovering data patterns and is commonly the starting point of data mining. This technique converts raw data into structured data and allows for the usage of various data mining methods that discover hidden patterns.

• Decision tree

Decision tree is a predictive technique whose pattern resembles a tree. Each branch is structured as a question and each leaf — as a part of data related to the answer. Decision tree is commonly used for prediction and exploration analysis. It is good at relating databases and stops “growing” if the segment contains a single record, or if all the records share the same features, or if the growth cannot branch further.

• Neural networks

Neural network is another technique used as the starting point of data mining. As the name suggests, neural networks are related to AI and, hence, require the user to know some basic answers. E.g., what are the nodes and how are they connected? A neural network comprises the node and the link, where the node is connected to the neuron in the human brain, with the link to the neuron connections.

Since neural networks comprise many interconnected neurons that form network architecture, they are not easily understood by the average user. Still, they remain one of the most precise predictive modelling techniques. Because of that, many businesses are applying it either as a solution integrated into a single app, or they accompany it with expert BI consulting services.

• Association rule

As the name portends, the association rule finds associations between two or more variables. The technique is useful for discovering hidden patterns and identification of frequently occurring variables. The association rule answers two basic questions:
• How often is the rule applied?
• How often is the rule correct?

There are three basic types of the rule: quantitative, multi-level and multi-dimensional. All of them are most commonly used for finding sales patterns.

• Classification

In data science, classification is one of the most frequently used data mining techniques. It contains a number of pre-classified samples used to create a model that will later be used to help classify larger datasets. It works similarly to clustering and uses either a neural network or a decision tree.

Classification comprises two phases: learning and classification. There are numerous classification sub-types, best known of which include Support Vector Machines (SVM), classification based on associations, classification by decision tree induction and Bayesian classification.

The importance of automated data mining techniques

Automated data mining techniques are what every business needs in order to always be able to make data-driven decisions. Data automation is rapidly gaining momentum, as speed, efficiency and affordability are not to be taken lightly. Minerra will help you achieve best business results with the help of expert BI consulting and cutting-edge automated analysis tools.

Contact Minerra for analytics training and mentoring!