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Aggregation in Data Mining

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  • Difficulty Level : Medium
  • Last Updated : 22 Sep, 2021
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Aggregation in data mining is the process of finding, collecting, and presenting the data in a summarized format to perform statistical analysis of business schemes or analysis of human patterns. When numerous data is collected from various datasets, it’s crucial to gather accurate data to provide significant results. Data aggregation can help in taking prudent decisions in marketing, finance, pricing the product, etc. Aggregated data groups are replaced using statistical summaries. Aggregated data being present in the data warehouse can help one solve rational problems which in turn can reduce the time strain in solving queries from data sets.

This article will explain the aggregation in data mining, their process, and its applications.

How does Data aggregation work:

Data Aggregation is a need when a dataset as a whole is useless information and cannot be used for analysis. So, the datasets are summarized into useful aggregates to acquire desirable results and also to enhance the user experience or the application itself. They provide aggregate measurements such as sum, count and average. Summarized data helps in the demographic study of customers, their behavior patterns. Aggregated data help in finding useful information about a group after they are written as reports. It also helps in data lineage to understand, record and visualize data which in turn help in tracing the root cause of errors in data analytics. There is no specific need for an aggregated element to be number. We can also find the count of non-numeric data. Aggregation must be done for a group of data and not based on individual data.

Examples of aggregate data:

  • Finding the average age of customer buying a particular product which can help in finding out the targeted age group for that particular product. Instead of dealing with an individual customer, the average age of the customer is calculated.
  • Finding the number of consumers by country. This can increase sales in the country with more buyers and help the company to enhance its marketing in a country with low buyers. Here also, instead of an individual buyer, a group of buyers in a country are considered.
  • By collecting the data from online buyers, the company can analyze the consumer behavior pattern, the success of the product which helps the marketing and finance department to find new marketing strategies and planning the budget.
  • Finding the value of voter turnout in a state or country. It is done by counting the total votes of a candidate in a particular region instead of counting the individual voter records.

Data aggregators:

Data Aggregators are a system in data mining that collects data from numerous sources, then processes the data and repackages them into useful data packages. They play a major role in improving the data of customer by acting as an agent. It helps in the query and delivery process where the customer requests data instances about a certain product. The aggregators provide the customer with matched records of the product. Thereby the customer can buy any instances of matched records.

Working of Data aggregators:


The working of data aggregators takes place in three steps:

  • Collection of data: Collecting data from different datasets from the enormous database. The data can be extracted using IoT(internet of things) such as
    • Communications in social media
    • Speech recognition like call centers
    • Headlines of a news
    • Browsing history and other personal data of devices.
  • Processing of data: After collecting data, the data aggregator finds the atomic data and aggregates it. In the processing technique, aggregators use various algorithms from the field of Artificial Intelligence or Machine learning techniques. It also incorporates statistical methods to process it, like the predictive analysis. By this, various useful insights can be extracted from raw data.
  • Presentation of data: After the processing step, the data will be in a summarized format which can provide a desirable statistical result with detailed and accurate data.

Choice of manual or automated data aggregators:

Data aggregation can also be done by manual method. When one starts a new company, one can opt manual aggregator by using excel sheets and by creating charts to manage performance, budget, marketing etc.

Data aggregation in a well-established company calls the need for middleware, a third party software to implement the data automatically using tools of marketing.

But when large datasets are encountered, a Data Aggregator system is a need to provide accurate results.

Types of Data Aggregation:

Types of data aggregation

  • Time aggregation: It provides the data point for single resources for a defined time period.
  • Spatial aggregation: It provided the data point for a group of resources for a defined time period.

Time intervals for data aggregation process:

  • Reporting period: The period in which the data is collected for presentation. It can either be a data point aggregated process or simply raw data. E.g. The data is collected and processed into a summarized format in a period of one day from a network device. Hence the reporting period will be one day.
  • Granularity: The period in which data is collected for aggregation. E.g. To find the sum of data points for a specific resource collected over a period of 10 mins. Here the granularity would be 10 mins. The value of granularity can vary from minute to month depending upon the reporting period.
  • Polling period: The frequency in which resources are sampled for data. E.g. If the group of resources can be polled every 7 minutes which means data points for each resource is generated every 7 minutes. Polling period and Granularity comes under spatial aggregation.

Applications of Data Aggregation:

  • Data aggregation is used in many fields where a large number of datasets are involved. It helps in making fruitful decisions in marketing or finance management. It helps in the planning and pricing of products.
  • Efficient use of data aggregation can help in the creation of marketing schemes. E.g. If the company is performing ad campaigns on a particular platform, they must deeply analyze the data to raise sales. The aggregation can help in analyzing the execution over a respective time period of campaigns or a particular cohort or a particular channel/platform. This can be done in three steps namely Extraction, Transform, Visualize.

Workflow of Data Analysis in SaaS Applications.

  • Data aggregation plays a major role in retail and e-commerce industries by monitoring the competitive price. In this field, to keeping track of its fellow company is a must. Like a company should collect details of pricing, offers etc. of other companies to know what its competitive company is up to. This can be done by aggregating data from a single resource like its competitor website.
  • Data aggregation plays an impactful role in the travel industry. It comprises research about the competitor and gaining intelligence in marketing to reach people, image capture from their travel websites. It also includes customer sentiment analysis which helps to find the emotions and satisfaction based on linguistic analyses. Failed data aggregation in this field can lead to the declined growth of the travel company.
  • For the business analysis purpose, the data can be aggregated into summary formats which can help the head of the firm to take correct decisions for satisfying the customers. It helps in inspecting groups of people.

Data Aggregation with Web Data Integration (WDI):

Web Data Integration(WDI) is a time-consuming nature in the data mining field where the data from different websites is aggregated into a single workflow. By using WDI, the time taken to aggregate data can be broken down to minutes which increases accuracy and thereby prevent human-made errors. By following the use cases provided by varied fields, the company can extract data from other sites to increase efficiency and accuracy. It can be done whenever the company wants in the places wherever they need. The inbuilt quality control in WDI helps in enhancing accuracy. It not only aggregates but cleans the data, also prepares it in useful forms for integration or analysis of data. If a company wants accuracy in dealing with data, WDI is the inevitable choice.

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