How Data Entry Outsourced Deploys the Most Popular Data Mining Algorithms

Data Mining is becoming an essential part of business processes for any organization. Discovering patterns in data that drive business competition as the core function of data mining for modern businesses.

At the heart of data mining are its algorithms, which help to analyze data to make predictions. There are many popular algorithms out there today that are serving as powerful tools to help businesses understand patterns in data and use this information to drive business growth opportunities in a variety of applications. By outsourcing data mining, you can have access to teams of professionals who understand how to bring value to your business by using structured data mining algorithms for the right purposes.

Data Entry Outsourced (DEO) considers unique client objectives to ensure that the algorithms identify the best combination of variables to not only mine data, but to answer critical business questions.

Understanding Data Mining Algorithms

Data Mining algorithms utilize both Supervised Learning and Unsupervised Learning, both of which are derivatives of Machine Learning. To understand data mining algorithms, it's helpful to look into some background about these two forms of Machine Learning.

In Supervised Learning, the most prevalent regarding widespread industrial applications, a training set of data is provided that helps to guide the machine as it works out the steps from input to output. Unsupervised Learning is much more complicated and interprets and finds solutions in data on its own without any datasets guiding the machine. The outcomes are also mostly unknown.

Supervised and Unsupervised Learning are the technologies behind the most effective and popular data mining algorithms that serve as the backbone and accelerant for their functionality.

Structured Data Mining Algorithms at DEO

Context-rich data mining defines the accuracy and usefulness of the result. It is important to understand the nuances of data sets and apply structured data mining algorithms to answering important questions rather than simply plugging a data set into an algorithm. Algorithms don't replace human judgment, and data mining techniques are best performed with human interpretation to increase the reliability of outcomes.

  • C4.5: This is a classification algorithm, part of supervised learning, building a decision tree from training data sets. C4.5 uses a single-pass pruning process to mitigate over-fitting.
  • K-Means: Uses cluster analysis and is considered unsupervised learning, where the clusters are "learned" without any input data. K-Means is used for its simplicity, grouping data based upon similarities.
  • Support Vector Machines: Similar to C4.5, but SVM does not use decision trees. Instead, it classifies data into two classes by learning a hyperplane, functioning similarly to an equation of a line, except it is projected on a 3-dimensional space.
  • Apriori: Part of unsupervised learning and is applied to large data sets to learn connections among variables within data. Also called association rule learning.
  • Expectation Maximization (EM): Used as a clustering algorithm (and part of unsupervised learning) that deploys statistical modeling to group data by examining the possibility of viewing observed data while evaluating the framework of a statistical model with unobserved variables.
  • PageRank: An unsupervised learning method that counts the number of times objects are linked together by performing linked analysis. Employed if the objective is to discover relative importance, priority, ranking or applicability.

Though correct statistical and computational components are factored into the data mining process, the underlying data can still be undermined by misunderstanding, nullifying an entire project result. Guiding algorithm models are critical to prevent implementation of limited algorithms that restrict findings and create problematic analytical scenarios.

Augmenting Algorithms with Industry Expertise

Data Entry Outsourced features educated and experienced professionals who understand the complexities of structured data mining algorithms, and how to apply those algorithms to determine the best way to serve business needs and inform business decisions at a strategic level. Contact us today to learn more about our offerings and customized engagement model.

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