What is Data Mining?
Data mining is a popular technological innovation that converts piles of data into useful knowledge that can help the data owners/users make informed choices and take smart actions for their own benefit. In specific terms, data mining looks for hidden patterns amongst enormous sets of data that can help to understand, predict, and guide future behavior. A more technical explanation: Data Mining is the set of methodologies used in analyzing data from various dimensions and perspectives, finding previously unknown hidden patterns, classifying and grouping the data and summarizing the identified relationships.
The elements of data mining include extraction, transformation, and loading of data onto the data warehouse system, managing data in a multidimensional database system, providing access to business analysts and IT experts, analyzing the data by tools, and presenting the data in a useful format, such as a graph or table. This is achieved by identifying relationship using classes, clusters, associations, and sequential patterns by the use of statistical analysis, machine leaning and neural networks.
Data can generate revenue. It is a valuable financial asset of an enterprise. Businesses can use data mining for knowledge discovery and exploration of available data. This can help them predict future trends, understand customer’s preferences and purchase habits, and conduct a constructive market analysis. They can then build models based on historical data patterns and garner more from targeted market campaigns as well as strategize more profitable selling approaches. Data mining helps enterprises to make informed business decisions, enhances business intelligence, thereby improving the company’s revenue and reducing cost overheads.
Data mining is also useful in finding data anomaly patterns that are essential in fraud detection and areas of weak or incorrect data collation/ modification. Getting the help of experienced data entry service providers in the early stages of data management can make the subsequent data mining easier.
The art of data mining has been constantly evolving. There are a number of innovative and intuitive techniques that have emerged that fine-tune data mining concepts in a bid to give companies more comprehensive insight into their own data with useful future trends. Many techniques are employed by the data mining experts, some of which are listed below:
Data mining relies on the actual data present, hence if data is incomplete, the results would be completely off-mark. Hence, it is imperative to have the intelligence to sniff out incomplete data if possible. Techniques such as Self-Organizing-Maps (SOM’s), help to map missing data based by visualizing the model of multi-dimensional complex data. Multi-task learning for missing inputs, in which one existing and valid data set along with its procedures is compared with another compatible but incomplete data set is one way to seek out such data.
Multi-dimensional preceptors using intelligent algorithms to build imputation techniques can address incomplete attributes of data.
This is a scoreboard, on a manager or supervisor’s computer, fed with real-time from data as it flows in and out of various databases within the company’s environment. Data mining techniques are applied to give live insight and monitoring of data to the stakeholders.
Databases hold key data in a structured format, so algorithms built using their own language (such as SQL macros) to find hidden patterns within organized data is most useful. These algorithms are sometimes inbuilt into the data flows, e.g. tightly coupled with user-defined functions, and the findings presented in a ready-to-refer-to report with meaningful analysis.
A good technique is to have the snapshot dump of data from a large database in a cache file at any time and then analyze it further. Similarly, data mining algorithms must be able to pull out data from multiple, heterogeneous databases and predict changing trends.
This concept is very helpful to automatically find patterns within the text embedded in hordes of text files, word-processed files, PDFs, and presentation files. The text-processing algorithms can for instance, find out repeated extracts of data, which is quite useful in the publishing business or universities for tracing plagiarism.
A data warehouse or large data stores must be supported with interactive and query-based data mining for all sorts of data mining functions such as classification, clustering, association, prediction. OLAP (Online Analytical Processing) is one such useful methodology. Other concepts that facilitate interactive data mining are analyzing graphs, aggregate querying, image classification, meta-rule guided mining, swap randomization, and multidimensional statistical analysis.
While selecting or choosing data mining algorithms, it is imperative that enterprises keep in mind the business relevance of the predictions and the scalability to reduce costs in future. Multiple algorithms should be able to be executed in parallel for time efficiency, independently and without interfering with the transnational business applications, especially time-critical ones. There should be support to include SVMs on larger scale.
There are many ready made tools available for data mining in the market today. Some of these have common functionalities packaged within, with provisions to add-on functionality by supporting building of business-specific analysis and intelligence.
This is very popular since it is a ready made, open source, no-coding required software, which gives advanced analytic s. Written in Java, it incorporates multifaceted data mining functions such as data preprocessing, visualization, predictive analysis, and can be easily integrated with WEKA and R-tool to directly give models from scripts written in the former two.
This is a JAVA based customization tool, which is free to use. It includes visualization and predictive analysis and modeling techniques, clustering, association, regression and classification.
Python is very popular due to ease of use and its powerful features. Orange is an open source tool that is written in Python with useful data analytic s, text analysis, and machine-learning features embedded in a visual programming interface. NTLK, also composed in Python, is a powerful language processing data mining tool, which consists of data mining, machine learning, and data scraping features that can easily be built up for customized needs.
Primarily used for data preprocessing – i.e. data extraction, transformation and loading, Knime is a powerful tool with GUI that shows the network of data nodes. Popular amongst financial data analysts, it has modular data pipe lining, leveraging machine learning, and data mining concepts liberally for building business intelligence reports.
Data mining tools and techniques are now more important than ever for all businesses, big or small, if they would like to leverage their existing data stores to make business decisions that will give them a competitive edge. Such actions based on data evidence and advanced analytics have better chances of increasing sales and facilitating growth. Adopting well-established techniques and tools and availing the help of data mining experts shall assist companies to utilize relevant and powerful data mining concepts to their fullest potential.
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