EFFICIENT DATA EXTRACTION USING ARTIFICIAL INTELLIGENCE
Download as PPTX, PDF1 like342 views
The document discusses efficient data extraction using artificial intelligence through web mining techniques, focusing on supervised and unsupervised learning methods. It highlights the role of neural networks and self-organizing maps (SOM) in understanding user behavior and clustering web data, along with a comparison showing the effectiveness of supervised learning over unsupervised methods. The findings suggest that SOM is superior in handling larger datasets compared to k-means, emphasizing the importance of data visualization in AI applications.
![Method 2- Unsupervised Learning
❖ Clustering using SOM
The self-organizing maps (SOM) introduced ]are deemed
as being highly effective as a sophisticated
visualization tool for visualizing high dimensional,
complex data with inherent relationships between the
various features comprising the data.
The SOM‟s output emphasizes the salient features of the
data and subsequently leads to the automatic
formation of clusters of similar data items.](https://image.slidesharecdn.com/airesearchpresentation-171207151718/85/EFFICIENT-DATA-EXTRACTION-USING-ARTIFICIAL-INTELLIGENCE-13-320.jpg)
EFFICIENT DATA EXTRACTION USING ARTIFICIAL INTELLIGENCE
- 1. EFFICIENT DATA EXTRACTION USING ARTIFICIAL INTELLIGENCE Deepak D. Upadhyay
- 2. Outlines ● Introduction to Web Mining For Extraction ● Purpose ● Method 1- Supervised Learning ● Method 2- Unsupervised Learning ● Comparison
- 3. Introduction to Web Mining for Extraction ● Web mining describes the practice, of conservative; data mining techniques onto the web resources and has facilitated the further development of these techniques to consider the specific structures of web data. ● The analysed web resources contain the actual web site and the hyperlinks connecting these sites and the path that online users take on the web to reach a distinct site.
- 4. Continue.. ● Web usage mining then refers to the deduction of useful knowledge from the data inputs. While the input data are mostly web server logs and other primarily technically position data, the expected output is an understanding of user behaviour in the domain of online data search, online shopping, online learning etc.
- 5. Purpose ● web usage mining that helps to deal with certain web scaling problems such as user trend analysis of surfing, traffic flow analysis, distributed control handling, web traffic management and many more. ● Session tracking and website reorganization, distributed traffic sharing on distributed servers can be identified and analysis based on web data can be possible using concepts of neural network.
- 6. Continue.. ● Neural network is far different from static networks in which each node is self-intelligent, hence the network becomes intelligent. So, web users can use this network more and more.
- 7. Method 1- Supervised Learning ● In supervised learning the task is to automatically induce a model based on a set of N instances, called training data. ● This model then will be used to assign labels to new instances with unknown labels using only the value of their predictor variables. ● Artificial neuronal network is based on simulating the structure and behaviour of the biological neuronal networks.
- 8. Two Approach for Web Mining in AI Approach 1- Neuro-Fuzzy Approach for Web Mining Approach 2- Reduction of Stages on Neuro-Fuzzy after Backpropagation implementation
- 9. Neuro-Fuzzy Approach for Web Mining
- 11. Continue.. ● If any Web-mining researches apply this Back propagations, then can easily obtained best result than any implemented Web mining techniques because of top down and bottom-up weights. ● Also using Back Propagation, it is more beneficiary to minimize the number of steps in Web mining as compare to neuro-fuzzy approach.
- 12. Continue.. ● As neuro-fuzzy approach uses five major steps to produce the Webusage pattern forecast, and Web- usage data analyzer; named Web-log data collection, data preprocessing, self-organizing map, Web-usage data cluster, and fuzzy inference system ● But Back propagations use only three steps as Web- log data collection, data pre-processing, and Back propagations itself.
- 13. Method 2- Unsupervised Learning ❖ Clustering using SOM The self-organizing maps (SOM) introduced ]are deemed as being highly effective as a sophisticated visualization tool for visualizing high dimensional, complex data with inherent relationships between the various features comprising the data. The SOM‟s output emphasizes the salient features of the data and subsequently leads to the automatic formation of clusters of similar data items.
- 14. Continue.. ● The Self-Organizing Map (SOM) has proven to be one of the most powerful algorithms in data visualization and exploration. Application areas include various fields of science and technology, e.g., complex industrial processes, telecommunications systems, document and image databases, and even financial applications. ● The SOM maps the high- dimensional input vectors onto a two-dimensional grid of prototype vectors and orders them.
- 15. Continue.. ● For a human interpreter, the ordered prototype vectors are easier to visualize and explore than the original data. The SOM has been widely implemented in various software tools, Post-processing the SOM extracts qualitative or quantitative information of the data.
- 16. Fig - Applying SOM in Data Mining
- 17. Table 1. Comparison with respect to SSE with Different clusters and cases of K-Means and SOM
- 18. Continue.. ● K-Means cover more Urls but SOM works better for larger number of cases. With increase in data, learning process of SOM becomes more accurate and we can consider larger number of clusters. SOM is also efficient in time as compared to K-Means. Thus we can conclude that SOM has better performance than K-Mean
- 19. Comparison ● supervised learning is much effective than unsupervised learning. Previously, unsupervised extraction used extraction patterns that make assumptions about the regularity of the structure in the data. We relax this assumption by exploiting reference sets to aid the extraction.
- 20. Continue.. ● SOM used for clustering is much faster and accurate which helps us further in artificial neural network mining which going to analyse pattern defined in the training set and further will be compared many unorganised testing set. The comparison will go under the process of pre-processing, classification ,clustering and analysing.