Data Mining

Data Mining

The manual extraction of patterns from data has occurred for centuries. Early methods of identifying patterns in data include Bayes' theorem (the 1700s) and regression analysis (1800s). The proliferation, ubiquity and increasing power of computer technology have dramatically increased data collection, storage, and manipulation ability. As data sets have grown in size and complexity, direct "hands-on" data analysis has increasingly been augmented with indirect, automated data processing, aided by other discoveries in computer science, especially in the field of machine learning, such as neural networks, cluster analysis, genetic algorithms (the 1950s), decision trees and decision rules (1960s), and support vector machines (1990s). Data mining is the process of applying these methods with the intention of uncovering hidden patterns in large data sets. It bridges the gap from applied statistics and artificial intelligence (which usually provide the mathematical background) to database management by exploiting the way data is stored and indexed in databases to execute the actual learning and discovery algorithms more efficiently, allowing such methods to be applied to ever-larger data sets.

Applications of Data mining include;

  1. Database Marketing and Targeting
  2. Sentiment Analysis
  3. Qualitative Data Mining (QDM)
  4. Recommendation Systems, and more.

Data Mining in Database Marketing And Targeting

In today’s world of increasing demands and high competitiveness, target marketing has become a necessity. It is very essential to make marketing strategies more customer-oriented for product development. For a successful business, identification of high-profit and low-risk customers, retaining those customers, and introducing new customers is a vital task. 
 Customer analysis is a crucial phase for companies in order to create new campaigns for their existing customers. Companies are able to group or cluster certain customers who have similar features. This may assist companies to make better marketing strategies over certain customer groups. 
Increasing the leads for the company is an important task for introducing new customers. Lead generation is the way in which the company collects contact information from potential customers. Once leads have been generated, marketing and sales work together to convert leads into customers. 
 Using data mining techniques, we propose an approach through which we can discover patterns and identify the characteristics of customers so as to enhance customer satisfaction and formulate marketing strategies to increase profitability. Since there are many different kinds of customers with different kinds of needs and preferences, performing market segmentation is necessary: divide the total market, choose the best segments, and design strategies for profitability, serving the chosen segment better than the company’s competitors do.  Data mining technologies and techniques can be used for recognizing and tracking patterns within data to help business sift through layers of seemingly unrelated data for a meaningful relationship.

Data Mining in Sentiment Analysis

Sentiment Analysis examines the problem of studying texts, like posts and reviews, uploaded by users on microblogging platforms, forums, and electronic businesses, regarding the opinions they have about a product, service, event, person, or idea. There are lots of tools that analyze social mentions, user's opinions, and the language they use to describe certain products and services to detect sentiment analysis.

Qualitative Data Mining (QDM)

Qualitative data-mining (QDM), using the narrative data contained in child welfare case records, enables researchers to examine child welfare practice using relatively non-intrusive methods. QDM can increase our understanding of client populations and problems, child welfare worker actions, and case complexity. This paper reports on experiences from the Child Welfare Qualitative Data-Mining Project; outlines a seven-step guide to QDM methods; and describes how QDM can be used to enhance child welfare practice, research, and education.

Recommendation Systems

A recommender system, or a recommendation system (sometimes replacing 'system' with a synonym such as a platform or an engine), is a subclass of an information filtering system that seeks to predict the "rating" or "preference" a user would give to an item. They are primarily used in commercial applications. Recommender systems are utilized in a variety of areas and are most commonly recognized as playlist generators for video and music services like Netflix, YouTube, and Spotify, product recommenders for services such as Amazon, or content recommenders for social media platforms such as Facebook and Twitter. These systems can operate using a single input, like music, or multiple inputs within and across platforms like news, books, and search queries. There are also popular recommender systems for specific topics like restaurants and online dating. Recommender systems have also been developed to explore research articles and experts, collaborators, and financial services.