{"id":1259,"date":"2025-04-06T16:33:36","date_gmt":"2025-04-06T16:33:36","guid":{"rendered":"https:\/\/learnlearn.uk\/ibcs\/?page_id=1259"},"modified":"2025-04-06T16:34:56","modified_gmt":"2025-04-06T16:34:56","slug":"data-mining","status":"publish","type":"page","link":"https:\/\/learnlearn.uk\/ibcs\/data-mining\/","title":{"rendered":"Data Mining"},"content":{"rendered":"
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Intro<\/h2>\n
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Data Mining<\/h2>\n

Data mining is the process of discovering patterns, correlations, and trends by sifting through large amounts of data stored in repositories, using various techniques from machine learning, statistics, and database systems.<\/p>\n

It involves the extraction of hidden predictive information from large databases and is a powerful tool that can help companies focus on the most important information in their data warehouses.<\/p>\n\n<\/div>

Steps<\/h2>\n
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Data Mining Steps<\/h2>\n

1. Data Collection and Preparation<\/h4>\n

Gathering relevant data from various sources and preparing it for analysis. This step includes data cleaning, integration, and transformation.<\/p>\n

2. Data Exploration and Understanding<\/h4>\n

Using descriptive statistics and visualization techniques to better understand the nature of the data, its quality, and the underlying patterns.<\/p>\n

3. Model Building and Validation<\/h4>\n

Applying appropriate algorithms to discover patterns and relationships within the data.<\/p>\n

4. Deployment and Interpretation of Results<\/h4>\n

Using the patterns and relationships found in the data to make decisions or predictions. The interpretation of these results should align with business objectives and needs.<\/p>\n\n<\/div>

Cluster<\/h2>\n
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Cluster Analysis<\/h2>\n

This is a technique used to group sets of objects in such a way that objects in the same group (or cluster) are more similar to each other than to those in other groups. It’s widely used in statistical data analysis for various applications, such as pattern recognition, image analysis, and bioinformatics.<\/p>\n

Clustering does not use pre-labeled classes; instead, it identifies similarities between data points and groups them accordingly.<\/p>\n

Image source: ResearchGate<\/a><\/p>\n\n<\/div>

Classification<\/h2>\n
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Classification Analysis<\/h2>\n

This technique involves finding a model (or function) that describes and distinguishes data classes or concepts. The model is then used to predict the class of objects whose class label is unknown.<\/p>\n

It’s based on training data consisting of a set of training examples. Classification is common in applications where you need to categorize data into predefined labels, such as spam detection in email service providers.<\/p>\n\n<\/div>

Association<\/h2>\n
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Association Analysis<\/h2>\n

Association analysis is a rule-based method for discovering interesting relations between variables in large databases. It’s often used in market basket analysis to find relationships between items purchased together.<\/p>\n

For example,\u00a0if a customer buys X, they are likely to buy Y<\/i>.<\/p>\n

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Beer & Diapers Example<\/h2>\n

A retail store, through data mining and analysis of their sales data, discovered an interesting association between two seemingly unrelated products: beer and diapers. The analysis showed that these items were often purchased together, particularly on certain days of the week or times of day.<\/p>\n

The underlying reason suggested for this pattern was that men, who were tasked with buying diapers, also tended to buy beer for themselves at the same time.<\/p>\n

This insight led the store to strategically place these items closer together or run promotions on them simultaneously, thereby increasing sales of both products.<\/p>\n\n<\/div>

Link<\/h2>\n
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Link Analysis<\/h2>\n

Link analysis is a data analysis technique used in network theory that explores the relationships between objects in a network.<\/p>\n

The key concept behind link analysis is understanding how different\u00a0nodes\u00a0<\/b>(or entities) are\u00a0connected<\/b>\u00a0and the\u00a0strength<\/b>\u00a0or significance of these connections<\/b>.<\/p>\n

Example Usage<\/h4>\n