Data stream mining
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This document discusses data stream mining and techniques for handling continuous data streams. It notes that data streams arrive continuously in high volumes and require one-pass algorithms due to memory and time constraints. Traditional data mining techniques cannot be directly applied. The document outlines requirements for data stream mining including processing examples one at a time with limited memory and time. It describes basic techniques like sampling, load shedding and sketching. It also discusses forgetting mechanisms like sliding windows and decay functions to handle concept drift. Classification algorithms and tools for data stream mining are also summarized.
![Refferences
18
▪ [1] Geoff Hulten et al, Mining Time-Changing Data Streams
▪ [2] Qin Zhang et al, Towards Mining Trapezoidal Data Streams
▪ [3] Neha Gupta, Indrjeet Rajput, Stream Data Mining: A Survey
▪ [4] Johns Hopkins, Data Stream Mining: A Review of Learning Methods and Frameworks
▪ [5] Jiawei Han et al, Data mining: Concepts and Techniques
▪ [6] Albert Bife et alt, DATA STREAM MINING A Practical Approach
▪ [7] Oded Maimon, Dr. Lior Rokach, Data Mining and Knowledge Discovery Handbook
▪ [8] Neha Gupta, Indrjeet Rajput, Stream Data Mining: A Survey, International Journal of Engineering
Research and Applications
▪ [9] Dariusz Brzeziński, MINING DATA STREAMS WITH CONCEPT DRIFT](https://image.slidesharecdn.com/datastreammining-180117135716/85/Data-stream-mining-18-320.jpg)
Data stream mining
- 1. Data Stream Mining George Tzinos
- 2. Introduction ▪ Large amount of data streams every day. ▪ Efficient knowledge discovery of such data streams is an emerging active research area in data mining with broad applications. ▪ Data streams typically arrive continuously in high speed with huge amount and changing data distribution. ▪ New issues that need to be considered. ▪ Data mining techniques which require multiple scans of the entire data sets can not be applied directly to mine stream data, which usually allows only one scan and demands fast response time 2
- 3. 3 Network traffic Sensor data Call center records Applications
- 4. Requirements 1. Process an example at a time, and inspect it only once (at most) 2. Use a limited amount of memory 3. Work in a limited amount of time 4. Be ready to predict at any point 4
- 7. Traditional Techniques vs Stream 7 Traditional Stream No. of passes Multiple Single Processing time Unlimited Restricted Memory usage Unlimited Restricted Type of result Accurate Approximate
- 8. Basic Techniques 8 ▪ Sampling ▪ Load shedding ▪ Sketching ▪ Synopsis data structures ▪ Aggregation
- 9. Forgetting mechanisms 9 ▪ Should be able to react to the changing concept by forgetting outdated data, while learning new class descriptions ▪ How to select the data range to remember
- 10. Utilization of time and space 10 ▪ Sliding Window ▪ Algorithm Output Granularity (AOG)
- 11. Windowing techniques - 1 11 ▪ The most popular approach to dealing with time changing data involves the use of sliding windows. ▪ Windows provide a way of limiting the amount of examples introduced to the learner ▪ Eliminating those data points that come from an old concept.
- 12. Windowing techniques - 2 12
- 13. Windowing techniques - 3 (Fixed Window) 13 ▪ Each example updates the window and later the classifier is updated by that window. ▪ In the simplest approach sliding windows are of fixed size ▪ Include only the most recent examples from the data stream. ▪ With each new data point the oldest example that does not fit in the window is thrown away. ▪ When using windows of fixed size, the user is caught in a tradeoff. ▪ If he chooses a small window size the classifier will react quickly to changes, but may loose on accuracy in periods of stability ▪ Choosing a large size will result in increasing accuracy in periods of stability, but will fail to adapt to rapidly changing concepts.
- 14. Windowing techniques - 4 14 ▪ Weights: ▫ A simple way of making the forgetting process more dynamic is providing the window with a decay function that assigns a weight to each example. ▫ Older examples receive smaller weights and are treated as less important by the base classifier. ▫ ( Maintaining time-decaying stream aggregates ) ▪ FISH ▪ ADWIN
- 15. Classification in Data Steams 15 ▪ Classification, learning a model in order to assign labels to new, unlabeled data points is a well studied supervised machine learning task. ▪ Methods include naive Bayes, k-nearest neighbors, classification trees, support vector machines, rule-based classifiers and many more (Hastie et al. 2001). ▪ However, as with clustering these algorithms need access to the complete training data several times and thus are not suitable for data streams with constantly arriving new training data and concept drift.
- 16. Classification in Data Steams - 2 16 ▪ Wang et al. proposed a general framework for mining concept drifting data streams. ▪ Domingos et al., VFDT (Very Fast Decision Tree) ▪
- 17. Tools for Data Streams 17 ▪ Scikit Learn (Out of core) ▪ MOA (Massive Online Analysis)
- 18. Refferences 18 ▪ [1] Geoff Hulten et al, Mining Time-Changing Data Streams ▪ [2] Qin Zhang et al, Towards Mining Trapezoidal Data Streams ▪ [3] Neha Gupta, Indrjeet Rajput, Stream Data Mining: A Survey ▪ [4] Johns Hopkins, Data Stream Mining: A Review of Learning Methods and Frameworks ▪ [5] Jiawei Han et al, Data mining: Concepts and Techniques ▪ [6] Albert Bife et alt, DATA STREAM MINING A Practical Approach ▪ [7] Oded Maimon, Dr. Lior Rokach, Data Mining and Knowledge Discovery Handbook ▪ [8] Neha Gupta, Indrjeet Rajput, Stream Data Mining: A Survey, International Journal of Engineering Research and Applications ▪ [9] Dariusz Brzeziński, MINING DATA STREAMS WITH CONCEPT DRIFT