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Top 5 Must-Know Data Mining Fundamentals

Data Mining is an essential component of Big Data technologies and Big Data analysis techniques. Generally, the term Data Mining refers to data analysis from different perspectives and made to turn these data into useful information, establishing relationships between data or by identifying patterns . This information can then be used by companies to increase revenues or reduce costs. They can also be used to better understand customers to develop better marketing strategies.

Data Mining is based on complex and sophisticated algorithms to segment the data and assess future probabilities. Data Mining is also known as Knowledge Discovery in Data (discovery of knowledge in the data).

4 types of relations

Computer technologies have evolved so that transactional systems and analytical systems are separated. Data Mining ensures the junction between the two. Data Mining software analyzes relationships and patterns in stored transaction data based on user queries. Several types of analytical software are available: statistics, Machine Learning and Neural Networks. In general, there are four types of relations:

  • Classes – Stored data is used to locate data in predetermined groups. For example, a restaurant chain can undermine customer purchase data to determine when to place customer visits and what their usual orders are. This information can be used to increase traffic by offering daily menus.
  • Clusters – Data are grouped according to logical relationships or customer preferences. For example, data can be mined to identify market segments or customer affinity.
  • Associations – The data can be mined to identify associations. For example, the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat.
  • Sequential patterns – Data is mined to anticipate patterns of behaviours and trends. For example, an outdoor equipment seller can predict the likelihood that a backpack is purchased based on the purchase of sleeping bag and hiking boots.

The 5 major elements

Data Mining is based on five major elements:

  • The extraction, transformation, and loading transactional data on the Data Warehouse system.
  • Storage and management of data in a multidimensional database system.
  • Provide data access to business analysts and professionals in computer technologies.
  • Analyze data using a software application.
  • Present data in a useful format, such as a graph or table.

Data Mining process in 5 steps

When turning the major elements above into practical steps, Data Mining process can be broken down into five steps:

  • First, companies collect data and load it into the data warehouse.
  • Thereafter, they store and manage data on physical servers or in the cloud.
  • The Business analysts, management teams and IT professionals access this data and determine how they wish to organize.
  • Then, the application software allows you to sort data based on user results.
  • Finally, the end user presents the data in an easy to share as a chart or table.

6 levels of analysis

  • The artificial neural networks – Non-linear predictive models that learn through training and resemble biological neural networks in structure.
  • Genetic algorithms – Optimization techniques use processes such as genetic combination, mutation and natural selection in a design based on the concepts of natural evolution.
  • Decision trees – These trees like structures represent sets of decisions. These decisions generate rules for classifying a data set. The specific methods of decision trees include Classification and Regression Trees (CART) and Chi-Square Automatic Interaction Detection (CHAID). These two methods are used for the classification of a data set. They provide a set of rules that can be applied to a new dataset to predict which records will be the result. CART segments a data set by creating a division in two exits, while the CHAID cleaves together using chi-square tests for creating from several channels. In general, CART requires less data preparation than CHAID.
  • The method of the nearest neighbour – This technique classifies each for recording a set of data based on a combination of the classes k, similar to a set of historic data.
  • The induction rule – The extraction rules “if-then” from the data, based on statistical significance.
  • Data visualization – Visual interpretation of complex relationships in multidimensional data. Graphical tools are used to illustrate the data relationships.

The 3 main properties of Data Mining

There are three main properties of Data Mining:

  • Automatic discovery of patterns – Data Mining is based on the development of models. A model uses an algorithm to operate on a data set. The concept of automatic discovery refers to the execution of data mining models. The data mining models can be used to undermine the data on which they are built, but most types of models can be generalized to new data. The process for applying a model to new data is called scoring.
  • The prediction of likely outcomes – Many forms is predictive data mining. For example, a model can predict an outcome based on education and other demographic factors. Predictions have an associated probability. Some forms of Predictive Data Mining generate rules that are the requirements to get a result. For example, a rule can specify that a person with a bachelor living in a particular neighborhood has a probability of having a better salary than the regional average.
  • Creating actionable information – Data Mining helps identify actionable information from large volumes of data . For example, an urban planner may use a model to predict the income based on demographic data to develop a plan for low-income households. A car rental agency can use a template to identify consumer segments to create a promotion targeting customers with high value.

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