Big Data Mining
What is Big Data Mining?
When doing Data Mining, one of the most important goals is to analyse huge quantities of data in order to uncover hidden patterns and connections in the data. The goal of our Big Data Mining service is to uncover new insights or patterns, as well as to create predictions about what will happen in the future.
Big Data in Cyber Security
Data mining has also proven to be a useful tool in cyber security solutions, allowing for the discovery of vulnerabilities and the collection of indicators for baselining
Web Data Mining
In addition to categorising online documents and identifying web pages, web mining contributes to the improvement of the power of web search engines.It is utilized for Web Searching, such as Google, Yahoo, and others, as well as Vertical Searching, such as FatLens, Become, and others. It is used to forecast user behaviour by analysing web traffic. Web mining is extremely beneficial to a specific Website and e-service, for example, landing page optimization. Web data mining consists of three different types of techniques of mining:
Web Content Mining
Extracting useful information from the content of the web documents– text, image, audio, video etc.
Web Structure Mining
Discovering structure data from the web- nodes, and hyperlinks as edges connecting related pages.
Web Usage Mining
Identifying or discovering interesting usage patterns from large data sets to understand user behaviors.
Social Media Data Mining
Using the Social Media Data Mining services provided by CryptoMize, you can ensure that you receive vital information relevant to your business interests by filtering through billions of posts, interactions, user profiles, metadata, and other data in order to analyse trends and keep you up to date with the latest developments.
Word Data Mining
Our certified experts are capable of extracting and analysing information from large quantities of text stored in databases, printed materials, or in Microsoft Word documents. Financial transaction data, legal problems, medical and scientific research are just a few of the sectors to which we have provided our services in the past.
What are the benefits of Big Data?
A data breach may be detected quickly by using data mining and security system data to analyse and predict risk patterns.
Big data analytics may assist in monitoring risks by tracking many system events. This technique may stop data leaks.
While real-time monitoring and vulnerability hunting is difficult, big data analytics may assist by automating the process.
Security insights are critical for maintaining effective cyber defense, for which analytics and reporting may assist us with.
What We Are Aiming For?
Our solutions utilize advanced analytical methods, cutting-edge technologies, and top industry practices to explore the hidden patterns in your data, to transform big data into actionable insights.
Our data mining services are available to everyone interested in improving their business performance. We will create a unique solution just for you based on your specific needs. With us, you won't have to worry about your project deadlines, as we offer flexible payment terms.
Frequently Asked Questions
Prediction is the process of extrapolating from past observations to make a reasonable guess about what the future holds. Data miners are trying to predict things like customer behaviour, disease outbreaks or credit risk. They'll take a set of information -- called a dataset -- and look for patterns that suggest what might happen in the future. A data miner will look at millions of records of people's buying habits, for example, and try to find clusters of customers who have similar tastes. Then she will make a prediction about the kinds of products they are likely to buy together.
Time Series Databases
World Wide Web(WWW)
Analysis:This method is used to find vital and relevant data and metadata. It's used to divide data into separate categories.
Association: It refers to a technique for identifying meaningful relationships (dependency modelling) among various variables in massive databases. This technique can assist you in uncovering hidden patterns in the data that can be used to discover variables within the data as well as the co-occurrence of many variables that appear frequently in the dataset.
Anomaly:This is the observation of data objects in a dataset that do not follow a predictable pattern or behave in a predictable manner. Outliers, novelties, noise, deviations, and exceptions are all terms used to describe anomalies. They frequently provide crucial and useful data.
Clustering: Clustering analysis is the process of identifying groups and clusters in data so that the degree of association between two objects is highest if they belong to the same group and lowest if they do not. Customer profiles can be created as a result of this analysis.
Regression: The technique of discovering and analysing the relationship between variables is known as regression analysis. If one of the independent variables is changed, it can help you comprehend how the characteristic value of the dependent variable changes.
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