Question

What are the different phases of the knowledge discovery from databases? Describe a complete application scenario in which new knowledge may be mined from an existing database of transactions.

Short answer

The different phases of Knowledge Discovery from Databases (KDD) are: Data Cleaning, Data Integration, Data Selection, Data Transformation, Data Mining, Pattern Evaluation, Knowledge Presentation. They can be applied in any situation where large amounts of stored data can potentially offer valuable insights when patterns and links are identified. An example scenario is a retail store analysing its transaction database to improve marketing efforts.

Step-by-step solution

Outlining Phases of Knowledge Discovery from Databases

The different phases of Knowledge Discovery from Databases (KDD) are: Data Cleaning, Data Integration, Data Selection, Data Transformation, Data Mining, Pattern Evaluation, and Knowledge Presentation.

Description of each Phase

Data Cleaning: Remove noise and inconsistent data. Data Integration: Combine multiple data sources. Data Selection: Data relevant for analysis is selected. Data Transformation: Transform data into a form suitable for mining. Data Mining: Apply intelligent methods to extract data patterns. Pattern Evaluation: Identify patterns representing knowledge. Knowledge Presentation: Visualize the knowledge.

Scenario for Application of KDD

Imagine a large retail store with an extensive database of transactions. It can use KDD to identify patterns such as which products are often bought together, or which times of day certain products get purchased. This information, new knowledge, can be used to strategize marketing efforts, such as promoting complementary items together or adjusting staffing based on peak shopping times.

Key concepts

Data Mining

Let's begin by exploring the exciting realm of Data Mining. Imagine being a treasure hunter, but instead of seeking gold or jewels, you unearth valuable patterns and insights hidden within mountains of data. That's what data miners do, they use sophisticated techniques to discover correlations, patterns, and trends among vast datasets. From predictive analytics to discovering consumer purchasing behaviors, data mining acts as a powerful tool that transforms raw data into meaningful information.

Data mining includes various methods like classification, clustering, regression, and association rule learning. Think of classification as sorting apples from oranges, whereas clustering is like grouping similar fruit together without labeling them. Regression analyses how variables, such as product price, relate to sales, while association rule learning is akin to realizing that many customers who buy bread also buy milk — it's all about finding those 'bought together' relationships. Armed with this knowledge, businesses can tailor their strategies to enhance customer experience, increase sales, and drive growth.

Data Cleaning

Now let's tackle the task of Data Cleaning, an essential step in ensuring the quality of data before further analysis. Imagine wanting to bake a cake with the freshest ingredients, but some are spoiled. To achieve the best taste, you'd need to remove the bad ones first. Similarly, data cleaning involves the removal of inaccurate, incomplete, or irrelevant parts of the data, often referred to as 'noise.'

This process can include filling in missing values, smoothing out noisy data, identifying or removing outliers, and resolving inconsistencies. It's a bit like decluttering your room to ensure you can find everything you need much more easily. Clean data leads to more accurate and trustworthy results, making the subsequent steps of data integration, transformation, mining, and pattern evaluation more effective.

Pattern Evaluation

Once valuable patterns are detected through data mining, Pattern Evaluation comes into play. This step is much like a chef tasting their dish to decide if it’s worthy of serving. It involves assessing and interpreting the discovered patterns to determine their potential usefulness and interest.

This phase is crucial because not all patterns unearthed during data mining will be significant or beneficial. Pattern evaluation criteria might include interestingness measures, such as how unexpected or novel the pattern is, as well as the pattern's potential to contribute to some business or research objectives. Effective pattern evaluation helps focus on the most meaningful insights, saving time and resources by avoiding the pursuit of unproductive leads.