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This document presents an overview of outlier detection, including definitions of outliers, common applications of outlier detection, and an algorithm called the depth-based approach. The depth-based approach models outliers as data objects located on the outer layers of convex hulls of the data space. It works by organizing data objects into convex hull layers and identifying outliers as those on outer layers, based on the assumption that outliers are located at the border of the data space while normal data is at the center.
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1. **Time Slots Allocation**: The core principle of TDM is to assign distinct time slots to each signal. During each time slot, the respective signal is transmitted, and then the process repeats cyclically. For example, if there are four signals to be transmitted, the TDM cycle will divide time into four slots, each assigned to one signal.
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3. **Frame Structure**: TDM data is organized into frames, where each frame consists of a set of time slots. Each frame is repeated at regular intervals, ensuring continuous transmission of data streams. The frame structure helps in managing the data streams and maintaining the synchronization between the transmitter and receiver.
4. **Multiplexer and Demultiplexer**: At the transmitting end, a multiplexer combines multiple input signals into a single composite signal by assigning each signal to a specific time slot. At the receiving end, a demultiplexer separates the composite signal back into individual signals based on their respective time slots.
### Types of TDM
1. **Synchronous TDM**: In synchronous TDM, time slots are pre-assigned to each signal, regardless of whether the signal has data to transmit or not. This can lead to inefficiencies if some time slots remain empty due to the absence of data.
2. **Asynchronous TDM (or Statistical TDM)**: Asynchronous TDM addresses the inefficiencies of synchronous TDM by allocating time slots dynamically based on the presence of data. Time slots are assigned only when there is data to transmit, which optimizes the use of the communication channel.
### Applications of TDM
- **Telecommunications**: TDM is extensively used in telecommunication systems, such as in T1 and E1 lines, where multiple telephone calls are transmitted over a single line by assigning each call to a specific time slot.
- **Digital Audio and Video Broadcasting**: TDM is used in broadcasting systems to transmit multiple audio or video streams over a single channel, ensuring efficient use of bandwidth.
- **Computer Networks**: TDM is used in network protocols and systems to manage the transmission of data from multiple sources over a single network medium.
### Advantages of TDM
- **Efficient Use of Bandwidth**: TDM all
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Depth based app
- 1. Depth based approach for outlier detection Presented By: Madhav solanki M.Tech(CSE) II Year 14815000210/1/2015 1
- 3. Introduction What is an outlier? Definition of Hawkins [Hawkins 1980]: “An outlier is an observation which deviates so much from the other observations as to arouse suspicions that it was generated by a different mechanism” 3
- 4. Application • Sample applications of outlier detection – Fraud detection • Purchasing behaviour of a credit card owner usually changes when the card is stolen • Abnormal buying patterns can characterize credit card abuse – Medicine • Unusual symptoms or test results may indicate potential health problems of a patient
- 5. cont……… – Public health • The occurrence of a particular disease, e.g. tetanus, scattered across various hospitals of a city indicate problems with the corresponding vaccination program in that city Sports statistics • In many sports, various parameters are recorded for players in order to evaluate the players’ performances • Outstanding (in a positive as well as a negative sense) players may be identified as having abnormal parameter values 5
- 6. cont……… Detecting measurement errors: • Data derived from sensors (e.g. in a given scientific experiment) may contain measurement errors • Abnormal values could provide an indication of a measurement error • Removing such errors can be important in other data mining and data analysis tasks 6
- 7. General application scenarios – Supervised scenario • In some applications, training data with normal and abnormal data objects are provided • There may be multiple normal and/or abnormal classes • Often, the classification problem is highly unbalanced – Semi-supervised Scenario • In some applications, only training data for the normal class(es) (or only the abnormal class(es)) are provided – Unsupervised Scenario • In most applications there are no training data available • In this tutorial, we focus on the unsupervised scenario 7
- 8. Depth-based Approach: • General idea:: – Search for outliers at the border of the data space but independent of statistical distributions – Organize data objects in convex hull layers – Outliers are objects on outer layers • Basic assumption:: – Outliers are located at the border of the data space – Normal objects are in the centre of the data space 8
- 9. Cont….. • Model [Tukey 1977] • – Points on the convex hull of the full data space have depth = 1 • – Points on the convex hull of the data set after removing all points with • depth = 1 have depth = 2 • – Points having a depth ≤ k are reported as outliers 9
- 10. References 10/1/2015 10 [1] Wikipedia [2] slidshare.com [3] http://try.rhapsody.com [4] http://www.dailytech.com