Knowledge Discovery in Data - KDD - (defined)

With user input and AI knowledge we create definable iterations of editable results from text being data.

Onil: Let's talk about Knowledge Discovery in Data also known as KDD and explain the types of knowledge we can discover by reading computer data, data from media, data from software programming and notes taken digitally and on paper...explain thoroughly the types of knowledge we can discover through Data Management.

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Knowledge Discovery in Data (KDD)

Knowledge Discovery in Data (KDD) is the process of discovering useful information and patterns in data. It encompasses data preparation, selection, parsing, cleaning, wrangling and transformation, as well as the application of data mining algorithms to extract hidden patterns and knowledge from data. (terms) The overall goal of KDD is to convert raw data into meaningful specific information that can inform decision-making.

Types of Knowledge Discovered in Data

1. Descriptive Knowledge

Descriptive knowledge involves summarizing the main characteristics of the data, providing an overview of its properties and structure. This type of knowledge answers questions such as:

• What are the most common trends in the data?
• What is the distribution of specific variables?
• What are the access points we can use to access more knowledge when prompted about it?

Examples:

• Statistical summaries (mean, median, mode, standard deviation). [mathematics]
• Frequency distributions and histograms. [graphs]
• Cluster profiles identifying groups of similar data points.
• Inferences used for further research and knowledge.

2. Predictive Knowledge

Predictive knowledge focuses on using historical data to make forecasts about future events. It involves building models that can predict outcomes based on input variables.

Examples:

• Predictive models in machine learning (e.g., regression analysis, decision trees, neural networks, correlations, inferences, connections).
• Time series forecasting to predict stock prices, weather conditions, or sales.
• Classification models to predict customer behavior, such as churn prediction.

3. Prescriptive Knowledge

Prescriptive knowledge provides recommendations for decision-making by considering the outcomes of different actions. It goes beyond predicting future outcomes by suggesting actions that could lead to desired results.

Examples:

• Optimization models for resource allocation, such as supply chain management. (using inductive vocabulary)
• Recommender systems suggesting products, movies, games, intellectual properties or articles. (to be used in further data collection and research & development)
• Decision support systems providing actionable insights based on data analysis. (programmable designs and concepts)

4. Diagnostic Knowledge

Diagnostic knowledge identifies the causes of observed phenomena. It explains why certain events happened and helps in understanding the underlying reasons behind patterns in the data.

Examples:

• Root cause analysis in manufacturing to identify reasons for defects. (or it’s converse)
• Fault detection systems in engineering and IT. (or symbolic analysis that can lead to language definable data)
• Medical diagnosis models identifying factors leading to a disease. (or the creation of biological structures that can heal and cure)

Knowledge Discovery from Different Data Sources

Computer Data

• System Logs: Analyzing logs for security breaches, performance issues, and user activities and input.
• Network Traffic: Monitoring traffic for anomalies indicating high target ideas.
• Usage Patterns: Understanding user behavior for optimizing system performance and enhancing user experience.

Media Data

• Text Analysis: Extracting themes, concepts, designs, sentiments, inferences and trends from articles, AI interaction, websites, accounts, blogs, social media, literature, knowledge and media from intellectual property.
• Image/Video Analysis: Detecting characters, objects, scenes, activities and language for applications in ideations, iterations, analysis of entertainment, and user systems.
• Audio Analysis: Transcribing and analyzing speech and sound for sentiment, emotion detection, and automatic transcription services and responses.

Data from Software Programming

• Code Analysis: Identifying ideas, thoughts, vocabulary, and code sense through dynamic code and text analysis.
• Version Control Data: Mining histories to understand developmental trends, contribution to adaptive vocabulary that lead to behaviors, and project lifespan evolution.
• Execution Traces: Analyzing runtime data to optimize performance, detect knowledge anomalies, and improve processes, inferences and iterations.

Notes and Structured Data

• Text Mining: Extracting key concepts, summaries, and relationships from structured text such as text notes, chats, research papers, academic data and personal inquiries.
• Sentiment Analysis: Analyzing opinions and sentiments expressed in written text to gauge mood, satisfaction, or public and personal opinion.
• Entity Recognition: Identifying and categorizing entities (names, dates, locations, numerics) in text for account knowledge organization and retrieval.

Summary

KDD is a powerful process for extracting meaningful insights from vast amounts of data and texts. The types of knowledge we can discover include:

• Descriptive knowledge provides summaries, overviews and inferences on texts.
• Predictive knowledge enables forecasting future events and ideations.
• Prescriptive knowledge offers actionable recommendations.
• Diagnostic knowledge explains observed patterns and iterates them.

By leveraging data from various sources such as computer logs and accounts, media content, software programming data, ideas, and artifacts, and structured texts and notes, we can uncover valuable insights that drive better decision-making, better idea iterations, imaginative vocabulary and planning for future designs and concepts across multiple domains, industries and disciplines.