addition of unit 1 3 4 5

unit 5/00_Index.md

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+# Unit 5: Advanced Data Mining Techniques
+
+Welcome to your simplified notes for Unit 5.
+
+## Table of Contents
+
+1. [[01_Ubiquitous_Data_Mining|Ubiquitous & Invisible Data Mining]]
+   - Mining everywhere (IoT, Mobile)
+   - Invisible Mining (Background processes)
+2. [[02_Web_Mining|Web Mining]]
+   - Content, Structure, and Usage Mining
+3. [[03_Spatial_and_Temporal_Mining|Spatial & Temporal Mining]]
+   - Mining location data (Maps/GIS)
+   - Mining time-based data (Trends)
+4. [[04_Other_Mining_Types|Other Mining Types]]
+   - Text, Visual, Audio, and Process Mining
+5. [[05_Applications_and_Impact|Applications & Social Impact]]
+   - Real-world uses (Healthcare, Retail)
+   - Privacy and Ethical concerns

unit 5/01_Ubiquitous_Data_Mining.md

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+# Ubiquitous and Invisible Data Mining
+
+## Ubiquitous Data Mining (UDM)
+**"Ubiquitous"** means existing everywhere.
+- **Definition**: Mining data from everyday objects and devices (Smartphones, IoT, Wearables) in real-time.
+- **Goal**: To provide insights anytime, anywhere, without you asking for it.
+- **Characteristics**:
+  - **Mobile**: Uses GPS and sensors.
+  - **Context-Aware**: Knows where you are and what time it is.
+  - **Real-Time**: Processes data instantly.
+
+### Examples
+- **Smartphones**: Google Maps predicting traffic.
+- **Wearables**: Smartwatches tracking your heart rate.
+- **Smart Homes**: Alexa learning your voice commands.
+
+## Invisible Data Mining
+- **Definition**: Mining that happens **silently** in the background. You don't see it happening.
+- **Why "Invisible"?**: It is embedded in apps and systems. You only see the result (like a recommendation).
+- **Examples**:
+  - **Amazon**: "People who bought this also bought..."
+  - **Google Search**: Auto-completing your sentence.
+  - **Banks**: Detecting fraud without you knowing.
+
+### Difference
+| Feature | Ubiquitous Mining | Invisible Mining |
+|---|---|---|
+| **Focus** | Mining **everywhere** (IoT, Mobile) | Mining **hidden** from user |
+| **Awareness** | You might know it's happening (e.g., wearing a watch) | You usually don't know |
+| **Key Tech** | Sensors, Mobile Devices | Software Algorithms, Background Processes |

unit 5/02_Web_Mining.md

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+# Web Mining
+
+**Web Mining** is using data mining techniques to discover useful information from the World Wide Web.
+
+## Types of Web Mining
+
+### 1. Web Content Mining
+- **What**: Mining the **actual content** of web pages.
+- **Data**: Text, images, audio, video.
+- **Example**: Analyzing reviews on Amazon to see if people like a product (Sentiment Analysis).
+
+### 2. Web Structure Mining
+- **What**: Mining the **links** (hyperlinks) between pages.
+- **Goal**: To find important pages (Authorities) and pages that link to many others (Hubs).
+- **Example**: Google's **PageRank** algorithm uses this to rank search results.
+
+### 3. Web Usage Mining
+- **What**: Mining **user activity** logs.
+- **Data**: Server logs, browser history, clicks.
+- **Example**: Analyzing which pages users visit most often and where they leave the site.

unit 5/03_Spatial_and_Temporal_Mining.md

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+# Spatial and Temporal Data Mining
+
+## Spatial Data Mining
+- **Spatial Data**: Data related to **location** or geography (Maps, GPS).
+- **Goal**: Finding patterns in space.
+- **Tools**: GIS (Geographic Information Systems).
+- **Examples**:
+  - Finding the best location for a new store.
+  - Tracking the spread of a disease on a map.
+
+## Temporal Data Mining
+- **Temporal Data**: Data related to **time**.
+- **Goal**: Finding patterns that change over time (Trends).
+- **Tasks**:
+  - **Trend Analysis**: Is the stock market going up or down?
+  - **Sequence Analysis**: "If event A happens, does event B follow?"
+- **Example**: Analyzing weather patterns over 10 years to predict climate change.

unit 5/04_Other_Mining_Types.md

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+# Other Types of Data Mining
+
+## 1. Text Mining
+- **Data**: Unstructured text (Emails, Tweets, Documents).
+- **Technique**: Natural Language Processing (NLP).
+- **Goal**: To understand meaning, sentiment, and topics.
+- **Example**: Classifying customer feedback as "Angry" or "Happy".
+
+## 2. Visual and Audio Mining
+- **Data**: Images, Videos, Sound.
+- **Goal**: To find patterns in visual or audio data.
+- **Example**: Face recognition in photos, or detecting keywords in a voice recording.
+
+## 3. Process Mining
+- **Data**: Event logs from business systems (ERP, CRM).
+- **Goal**: To see how a business process *actually* works vs how it *should* work.
+- **Example**: Finding out why it takes 5 days to approve a loan instead of 2.

unit 5/05_Applications_and_Impact.md

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+# Applications and Social Impact
+
+## Applications of Data Mining
+Data mining is used everywhere!
+
+1. **Healthcare**: Predicting diseases, finding side effects of drugs.
+2. **Retail (Market Basket Analysis)**: Placing Bread near Butter to increase sales.
+3. **Finance**: Detecting credit card fraud, approving loans.
+4. **Education**: Tracking student performance to help them improve.
+5. **Crime**: Identifying crime hotspots and predicting criminal behavior.
+
+## Social Impact and Issues
+
+### Positive Impact
+- **Convenience**: Personalized recommendations save time.
+- **Safety**: Fraud detection and medical diagnosis save money and lives.
+
+### Negative Impact (Ethical Issues)
+1. **Privacy Invasion**: Companies know too much about you.
+2. **Discrimination**: Profiling can lead to unfair treatment (e.g., denying loans based on where you live).
+3. **Security**: Large databases can be hacked (Data Breaches).
+4. **Manipulation**: Targeted ads can influence your behavior or political views.