{"id":3430,"date":"2023-02-15T06:03:15","date_gmt":"2023-02-15T06:03:15","guid":{"rendered":"https:\/\/sagaratechnology.com\/blog\/?p=3430"},"modified":"2023-03-15T06:13:55","modified_gmt":"2023-03-15T06:13:55","slug":"3-key-data-mining-techniques-and-how-businesses-use-them","status":"publish","type":"post","link":"https:\/\/sagaratechnology.com\/blog\/3-key-data-mining-techniques-and-how-businesses-use-them\/","title":{"rendered":"3 Key Data Mining Techniques and How Businesses Use Them"},"content":{"rendered":"\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" loading=\"lazy\" width=\"2000\" height=\"2000\" src=\"https:\/\/sagaratechnology.com\/blog\/wp-content\/uploads\/2023\/03\/4944103.jpg\" alt=\"3 Key Data Mining Techniques and How Businesses Use Them\" class=\"wp-image-3596\"\/><figcaption class=\"wp-element-caption\">PIcture: Freepik<\/figcaption><\/figure>\n\n\n\n<p>Businesses collect and store an unimaginable amount of data, but how do they turn all that data into insights that help them build a better business? Data mining, the process of sifting through massive amounts of data to identify hidden business trends or patterns, makes these transformational business insights possible.<\/p>\n\n\n\n<p>Data mining is not a new technology. Its roots have been traced to the 1930s, according to Hacker Bits. Still, the term became more widely used in the 1990s as businesses attempted to grapple with the ever-increasing amount of data our society was producing to derive value from it.<\/p>\n\n\n\n<p>The advent of modern computers and the application of data mining techniques meant businesses could finally analyze exponential amounts of data and extract non-intuitive, valuable insights, forecasting likely business outcomes, mitigating risks, and taking advantage of newly identified opportunities.&nbsp;<\/p>\n\n\n\n<p>Due to its usefulness across many industries and its critical role in business success, data mining is a promising career path. Companies need data scientists skilled in mining techniques who can present their findings in understandable ways.<\/p>\n\n\n\n<p>So what are the key techniques that aspiring data miners should know? Here are 10 data mining techniques that we will explore in detail:<\/p>\n\n\n\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_76 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<label for=\"ez-toc-cssicon-toggle-item-69e78f1524351\" class=\"ez-toc-cssicon-toggle-label\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/label><input type=\"checkbox\"  id=\"ez-toc-cssicon-toggle-item-69e78f1524351\"  aria-label=\"Toggle\" \/><nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/sagaratechnology.com\/blog\/3-key-data-mining-techniques-and-how-businesses-use-them\/#Clustering\" >Clustering<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/sagaratechnology.com\/blog\/3-key-data-mining-techniques-and-how-businesses-use-them\/#Association\" >Association<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/sagaratechnology.com\/blog\/3-key-data-mining-techniques-and-how-businesses-use-them\/#Data_Cleaning\" >Data Cleaning<\/a><\/li><\/ul><\/nav><\/div>\n<h3><span class=\"ez-toc-section\" id=\"Clustering\"><\/span><strong>Clustering<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Clustering is a technique used to represent data visually \u2014 such as in graphs that show buying trends or sales demographics for a particular product.<\/p>\n\n\n\n<h4><strong>What Is Clustering in Data Mining?<\/strong><\/h4>\n\n\n\n<p>Clustering refers to the process of grouping a series of different data points based on their characteristics. By doing so, data miners can seamlessly divide the data into subsets.<\/p>\n\n\n\n<h4><strong>Methods for Data Clustering<\/strong><\/h4>\n\n\n\n<ol>\n<li><strong>Partitioning method<\/strong>: This involves dividing a data set into a group of specific clusters for evaluation based on the criteria of each individual cluster. In this method, data points belong to just one group or cluster.<\/li>\n\n\n\n<li><strong>Hierarchical method<\/strong>: With the hierarchical method, data points are a single cluster grouped based on similarities. Those newly created clusters can then be analyzed separately from each other.&nbsp;<\/li>\n\n\n\n<li><strong>Density-based method<\/strong>: A machine learning method where data points plotted together are further analyzed, but data points themselves are labeled as a \u201cnoise\u201d and discarded.<\/li>\n\n\n\n<li><strong>Grid-based method<\/strong>: This involves dividing data into cells on a grid. As a result, grid-based clustering has a fast processing time.<\/li>\n\n\n\n<li><strong>Model-based method<\/strong>: In this method, models are created for each data cluster to locate the best data to fit that particular model.<\/li>\n<\/ol>\n\n\n\n<h4><strong>Examples of Clustering in Business<\/strong><\/h4>\n\n\n\n<p>Clustering helps businesses manage their data more effectively. For example, retailers can use clustering models to determine which customers buy particular products, on which days, and with what frequency. This can help retailers target products and services to customers in a specific demographic or region.<\/p>\n\n\n\n<p>Clustering can help grocery stores group products by various characteristics (brand, size, cost, etc.) and better understand their sales tendencies. It can also help car insurance companies that want to identify customers. In addition, banks and financial institutions might use clustering to understand better about their customers as well.<\/p>\n\n\n\n<h3><span class=\"ez-toc-section\" id=\"Association\"><\/span><strong>Association<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Association rules are used to find correlations, or associations, between points in a data set.&nbsp;<\/p>\n\n\n\n<h4><strong>What Is Association in Data Mining?<\/strong><\/h4>\n\n\n\n<p>Data miners use association to discover unique or interesting relationships between variables in databases. Association is often employed to help companies determine marketing research and strategy.<\/p>\n\n\n\n<h4><strong>Methods for Data Mining Association<\/strong><\/h4>\n\n\n\n<p>Two primary approaches using association in data mining are the single-dimensional and multi-dimensional methods.<\/p>\n\n\n\n<ol>\n<li><strong>Single-dimensional association<\/strong>: This involves looking for one repeating instance of a data point or attribute. For instance, a retailer might search its database for the instances a particular product was purchased.&nbsp;<\/li>\n\n\n\n<li><strong>Multi-dimensional association<\/strong>: This involves looking for more than one data point in a data set. That same retailer might want more information than what a customer purchased \u2014 such as their age, and method of purchase (cash or credit card).<\/li>\n<\/ol>\n\n\n\n<ol start=\"3\">\n<li><strong>Examples of Association in Business<\/strong><\/li>\n<\/ol>\n\n\n\n<p>The analysis of impromptu shopping behavior is an example of association. From data studies, retailers notice that parents shopping for childcare supplies are more likely to purchase specialty food or beverage items during the same trip. <\/p>\n\n\n\n<p>Association analysis carries many other uses in business. For retailers, it\u2019s particularly helpful in making purchasing suggestions. For example, if a customer buys a smartphone, tablet, or video game device, association analysis can recommend related items like cables or applicable software.<\/p>\n\n\n\n<p>In addition, the government uses associations to plan for public services and use census data.<\/p>\n\n\n\n<h3><span class=\"ez-toc-section\" id=\"Data_Cleaning\"><\/span><strong>Data Cleaning<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Data cleaning is the process of preparing data to be mined.<\/p>\n\n\n\n<h4><strong>What Is Data Cleaning in Data Mining?<\/strong><\/h4>\n\n\n\n<p>Data cleaning involves organizing, eliminating duplicate or corrupted data, and filling in any null values. When this process is complete, It is possible to gather and analyze the most important data..<\/p>\n\n\n\n<h4><strong>Methods for Data Cleaning<\/strong><\/h4>\n\n\n\n<ol>\n<li><strong>Verifying the data<\/strong>: This involves checking that each data point in the data set is in the proper format (e.g., telephone numbers).&nbsp;<\/li>\n\n\n\n<li><strong>Converting data types<\/strong>: This ensures data uniform across the data set. For instance, numeric variables only contain numbers, while string variables can contain letters, numbers, and characters.&nbsp;<\/li>\n\n\n\n<li><strong>Removing irrelevant data<\/strong>: This clears useless or inapplicable data so full emphasis can be placed on necessary data points.&nbsp;<\/li>\n\n\n\n<li><strong>Eliminating duplicate data points<\/strong>: This helps speed up the mining process by boosting efficiency and reducing errors.<\/li>\n\n\n\n<li><strong>Removing errors<\/strong>: This eliminates typing, spelling, and input errors that could negatively affect analysis outcomes.&nbsp;<\/li>\n\n\n\n<li><strong>Completing missing values<\/strong>: This provides an estimated value for all data and reduces missing values, which can lead to skewed or incorrect results.<\/li>\n<\/ol>\n\n\n\n<h4><strong>Examples of Data Cleaning in Business<\/strong><\/h4>\n\n\n\n<p>According to Experian, <a href=\"https:\/\/www.bls.gov\/ooh\/computer-and-information-technology\/computer-and-information-research-scientists.htm#tab-6\">95 percent of businesses have been impacted by poor data quality<\/a>. Working with incorrect data wastes time and resources increases analysis costs, and often leads to faulty analytics.<\/p>\n\n\n\n<p>Ultimately, no matter how great their models or algorithms are, businesses suffer when their data is incorrect, incomplete, or corrupted.<\/p>\n\n\n\n<p>Businesses looking for a competitive advantage often find data among their best resources, and data mining techniques are vital in bringing this resource to fruition. Mining allows businesses to harness the power of data, gain insight, detect patterns and anomalies, and find ways to be more productive.<\/p>\n\n\n\n<p>As we continue to produce a growing amount of diverse data, the ability to mine that data for insights will become increasingly important. Organizations generally want faster, more efficient ways to work, more methods to visualize data, and computing systems. As a result, many companies expect to increase their investment in analytics initiatives, which include data mining.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Businesses collect and store an unimaginable amount of data, but how do they turn all that data into insights that help them build a better business? Data mining, the process of sifting through massive amounts of data to identify hidden business trends or patterns, makes these transformational business insights possible. Data mining is not a&#8230;<\/p>\n","protected":false},"author":26,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[1],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.1 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>3 Key Data Mining Techniques and How Businesses Use Them -<\/title>\n<meta name=\"description\" content=\"Data mining are the process of sifting through massive amounts of data to identify hidden business trends or patterns.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sagaratechnology.com\/blog\/3-key-data-mining-techniques-and-how-businesses-use-them\/\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:title\" content=\"3 Key Data Mining Techniques and How Businesses Use Them -\" 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