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What are data mining techniques? How is data preprocessing used in data mining? What is exploratory data analysis? What is predictive analytics in data mining? What is document mining? How can AWS help with data mining techniques?

What are data mining techniques?

Data mining techniques enable organizations to uncover subtle patterns and relationships within their data. They convert raw data into practical knowledge that can be used to solve problems, analyze the future impact of business decisions, and increase profit margins. This guide explores various data mining techniques and how to implement them on AWS.

Organizations store and process large volumes of information from various business processes. Data mining helps them gain valuable insights from historical data with data modelling and predictive analytics. Modern data mining often uses artificial intelligence and machine learning learning (AI/ML) technologies to accelerate business insights and drive better results.

However, businesses face challenges when performing knowledge discovery with on-premise infrastructure. Specifically, they need to integrate data mining tools with diverse data sources, connect with third-party applications, and inform various stakeholders of the results, which conventional infrastructure does at an expensive cost.

AWS offers managed services that help organizations scale their data mining process on the cloud. We combine powerful data mining capabilities, generative AI expertise, and data governance best practices with Amazon SageMaker. This allows data scientists to unify data from diverse sources, run complex data analytics queries, and monitor data against security policies more effectively.

Besides improving data flow, organizations can deliver advanced analytics more affordably without having to provision their own infrastructure. For example, Lennar transformed its data foundation using Amazon Sagemaker Unified Studio and Amazon Sagemaker Lakehouse, enabling its data team to derive business insights more effectively.

Various data mining techniques are explained next, along with how AWS tools can help with them.

How is data preprocessing used in data mining?

Data preprocessing transforms raw data into a format that is understandable by data mining neural networks. It is a critical part of data mining because it significantly influences the performance of the data model. Often, raw data might contain errors, duplicates, and missing information that can negatively impact the model’s outcome. With data preprocessing, you can clean the data and remove such anomalies. Additionally, data scientists can select specific features that contribute to business insights and eliminate unnecessary information. For example, when predicting customer churn, you select features such as average monthly usage, the last login date, and the frequency of support requests. We refer to this feature as engineering, which enables you to reduce the compute resources required for data mining.

Amazon SageMaker Data Wrangler is a data preparation tool that helps you improve data quality and, subsequently, analytics outcomes. You can use Amazon SageMaker Data Wrangler across various data sources connected to your data pipeline. Instead of spending hours cleaning data, Amazon SageMaker Data Wrangler does it in minutes, thanks to its no-code approach. Here’s how to prepare data for your machine learning model with SageMaker Data Wrangler.

Step 1 — Select and query

Use the visual query builder to access and retrieve text, image, and tabular data across AWS and third-party storage. Then, apply findings in data quality reports to detect anomalies such as outliers, class imbalance, and data leakage.

Step 2 — Cleanse and enrich

Transform your data with prebuilt PySpark transformations and a natural language interface. Amazon SageMaker Data Wrangler supports common data transformations, including vectorizing text, featurizing datetime data, encoding, and balancing data. Additionally, you can easily create customized transformations to support your use case.

Step 3 — Visualize and understand

Validate the data prepared with charts, diagrams, and other visual tools. Then, run a quick analysis to predict the model’s outcome before actually training one.

What is exploratory data analysis?

Exploratory data analysis (EDA) is a data science technique that enables data scientists to uncover hidden patterns, identify meaningful relationships, and detect anomalies in data. Often, EDA is guided by visual tools, such as histograms, charts, and graphs. EDA’s purpose is rooted in providing guidance for subsequent data analysis. Additionally, it helps data scientists free their judgment from assumptions and biases.

Simply put, EDA provides evidence that can be observed through statistical modeling and techniques such as time-series analysis, spatial analysis, and scatter plots. Performing EDA, however, requires a suite of data mining tools that must work together in an integrated manner. Setup can be expensive.. 

Amazon SageMaker Unified Studio is a single AI and data platform that allows your team to build, deploy, and share data analytics workloads. You can use it to work with familiar AI/ML tools, storage, and analytics from AWS, including Amazon EMR, AWS Glue, Amazon Athena, Amazon Redshift, Amazon Bedrock, and Amazon SageMaker AI.

Below are ways you can accelerate exploratory data analysis (EDA with Amazon SageMaker Unified Studio.

  • Subscribe, manage, and set rules for data assets you want to use in training data analytics models.
  • Query data stored in data lakes, data warehouses, and other sources.
  • Create a workflow with a built-in visual interface to add transformation modules between data sources and the destination.

What is predictive analytics in data mining?

Predictive analytics in data mining utilizes discovered data patterns to forecast future outcomes. To do so, data is fed to machine learning models, which, based on their learned knowledge, make predictions that help businesses support their decisions. For example, finance companies use predictive analytics to forecast market trends, detect fraud, and assess credit risks.

Amazon SageMaker Canvas is a visual development tool that lets you train, test, and deploy predictive models at scale. It provides access to foundational models and custom machine learning (ML) algorithms, enabling the generation of accurate predictions for various use cases.

Additionally, you can build the entire data workflow with conversational language using Amazon Q Developer. It is a generative AI assistant that enables you to describe machine learning and data analytics tasks in everyday language. Then, it converts your descriptions into queries, SQL scripts, actionable steps, code recommendations, and more to help you work with AI and data more efficiently.

Below are models that you can build and deploy with Amazon SageMaker Canvas to enable predictive analytics.

Classification

Classification models can assign labels to previously unseen data based on characteristics they have learned. For example, an AI-powered customer support system can classify feedback as positive, negative, or neutral by analyzing words in the conversation. Amazon SageMaker Canvas supports classification models for various problem types, including text classification, image classification, anomaly detection, and object detection.

Association rule mining

Association rule mining (ARM) discovers the relationship between data points and can be used to augment a predictive analytics pipeline. For example, you can use ARM to run market basket analysis and find out which items are frequently bought together at a supermarket. Amazon SageMaker allows you to create your own custom ARM algorithms using frameworks like Python and deploy them within your AI/ML workflow on AWS.

Clustering

Clustering indirectly supports predictive analytics by grouping data based on similar attributes together. For example, you can cluster customers based on average spending value. Then, the segmented customers are used as one of the features in a predictive model. To cluster data, data scientists often use the K-means algorithm. Amazon SageMaker utilizes a modified version of the K-means algorithm, which yields more accurate results and enhanced scalability.

Anomaly detection

Machine learning models can be trained to detect outliers in data patterns. For example, factories utilize predictive models to identify potential failures in machines. Anomaly detection supports proactive mitigation actions, such as conducting preventive maintenance to prevent operational disruptions.

With Amazon SageMaker, you can detect abnormal patterns with the Random Cut Forest algorithm, which assigns low (normal) and high (abnormal) scores to data.

What is document mining?

Document mining is a machine learning technique that discovers, extracts, and analyzes text, image, or tabular data found in documents. Organizations can reduce costs, enhance customer experience, and boost operational efficiency by applying data mining technologies to the documents they store. For example, legal firms can automatically extract specific clauses from contracts using document mining.

You can apply ready-to-use document mining models with Amazon SageMaker Canvas. These models are pre-trained, which means you can integrate them into your data mining workflow without additional fine-tuning. Once set up, the model analyzes the raw data in the documents for meaningful patterns. Then, it extracts, categorizes, or labels it accordingly.

For example, the personal information detection model enables the detection of information such as addresses, bank account numbers, and phone numbers from textual data. Meanwhile, the expense analysis model retrieves information such as amount, date, and items from receipts and invoices.

Here’s how to apply document mining techniques with Amazon SageMaker Canvas.

  1. Create your SageMaker AI domain and turn on Canvas Ready-to-use models.
  2. Import the document datasets that you want to analyze. This allows you to create a data flow.
  3. Select a data mining model to generate predictions. You can make single or batch predictions from the setup.

How can AWS help with data mining techniques?

Data mining techniques enable businesses to uncover valuable insights from the data they generate, allowing them to make informed decisions. Successful data mining requires a streamlined data pipeline, which connects raw data from diverse sources to powerful AI/ML models.

The data pipeline automates data extraction, storage, cleaning, and transformation to ensure subsequent models receive high-quality and accurate data. Then, you apply various types of data mining techniques to derive meaningful insights.

Explore Amazon SageMaker to simplify complex data workflows and get predictive insights that enable better business outcomes.