Top (10) challenging problems in data mining

Top (10) challenging problems in data mining

1. Top (10) challenging problems in data mining Supervised by: Dr. Ali Haroun Prepared by : Ahmed Ramzi Rashid Ahmed Sedeeq Baker Master 2017-3-11

2. Suggestions Outlines : 2 Introduction Top 10 challenging Problems in data mining Conclusions

3. Introduction (1-1) : Data mining is sorting through data to identify patterns and establish relat-ionships. Data mining parameters include : - Association; - Sequence or path analysis; - Classification; - Clustering; - Forecasting. 3

4. Introduction (1-2) : 4 Data is Very complex So we have top 10 challenging Problems in data mining There is a different Way to extract The information A huge amount of data Data is power Many algorithms

5. - Top 10 challenging Problems in data mining (DM) : 1- Developing a Unifying Theory of Data Mining : 5 The developers could not have a structure that contains the different datamining algorithms . Knowledge To be verified Types of dataset Selection criterion Unified (DM) process Numeric Categorical Multimedia Text Akaike information criterion Clustering Classification Association

6. - Top 10 challenging Problems in data mining (DM) : 2- Scaling Up for High Dimensional Data and High Speed Data Streams : The problem begins when the data becomes huge and complex we need ultra-high dimensional classification problems (millions or billions of features ) Rather than we need Ultra-high speed data stream

7. In this problem we want to see how to efficiently and predict the direction of these data . In any design we must take care of this three master steps: 7 Practical design Predictor Information Learner (1) QIANG YANG ,10 Challenging problems in data mining research , International Journal of Information Technology & Decision Making , Vol. 5, No. 4 (2006) 597–604 . - Top 10 challenging Problems in data mining (DM) : 3- Mining Sequence Data and Time Series Data :

8. We have complex knowledge when we have mining data from multiple relation. In most domains, the object of interest are not independent of each other. The objects are not of a single type. 8 HTML has a tree structure (nested tags) Text has a list structure (sequence of words) Hyperlinks graph structure (Linked pages) Example domains Worldwide Web (1) Jarosław Stepaniuk , Rough – Granular in Knowledge Discovery and Data Mining , Volume 152 of the series , pp 99-110 . - Top 10 challenging Problems in data mining (DM) : 4. Mining Complex Knowledge from Complex Data :

9. 5.1 : Community and social networks : when we say community we must take important topics that are mining of social networks . The challenging to identify the problem is : It’s critical . Distributed . Snapshot . 9 5.2 : Mining in and for computer networks — high-speed mining of high-speed streams : This part studies how to provide a Good algorithm are and how to detecte an attack . DoS (Denial of Service) how to detected it and how to discriminate . We will discuss two part in this problem: (1) Qiang Yang, Hong Kong , 10 Challenging Problems in Data Mining Research , ICDM 2005 , pp 8. - Top 10 challenging Problems in data mining (DM) : 5 . Data Mining in a Network Setting :

10. Need to correlate the data seen at the various probes (such as in a sensor network ). The important problem is how to mine across multiple heterogeneous data sources. The goal is to minimize the amount of data shipped between the various sites, by combining data mining with game theory . 10 (1) Rao , Dr. S Vidyavathi , Distributed data mining and mining multi – agent data , International Journal on Computer Science and Engineering ,Vol. 02, No. 04, 2010, 1237-1244 . - Top 10 challenging Problems in data mining (DM) : 6 . Distributed Data Mining and Mining Multi-Agent Data :

11. 11 The world today is “resource-driven”. So how we could have a best understand and hence utilize about our environment . The researchers try to solve these problems : - Bioinformatics . - Spatial data . - Earthquakes . - Land slide . - Biological sequence . - Cancer prediction . () Pooja Shrivastava & Dr. Manoj Shukla , A Brief Survey On Data mining For Biological and Environmental Problems , International Journal of Scientific & Engineering Research, Volume 4, Issue 11, November-2013 , pp630-631 . - Top 10 challenging Problems in data mining (DM) : 7. Data Mining for Biological and Environmental Problems :

12. Data cleaning how to merge visual interactive and automatic (DM ) techniques together. 12 how to perform systematic documentation of data cleaning . Help users to avoid mistakes in (DM ). Create a methodology in (DM) . () QiangYang , 10 Challenging Problems in Data Mining Research , ICDM 2005 , pp 11 . - Top 10 challenging Problems in data mining (DM) : 8. Data Mining Process-Related Problems : Automate (DM) operations Combine techniques

13. 13 Knowledge integrity challenges Knowledge integrity challenges The challenges facing researchers Data are being mined Develop efficient algorithm to compare (before & after) knowledge contents . Not just evaluates the knowledge integrity But also measures to evaluate the knowledge integrity of individual patterns. How to mined the data with Ensure the user’s privacy Develop algorithms for estimating the impact of the data. () QIANG YANG , 10 CHALLENGING PROBLEMS IN DATA MINING RESEARCH , International Journal of Information Technology & Decision Making Vol. 5, No. 4 (2006) , pp603. - Top 10 challenging Problems in data mining (DM) : 9. Security, Privacy, and Data Integrity :

14. 14 Sampling Correct the bias Deal with special data Sampling and model building are not optimal . Here is the problem that how to correct the bias as we can. Deal with unbalanced and cost – sensitive data . Obtaining these costs relied on sampling method . () QIANG YANG , 10 CHALLENGING PROBLEMS IN DATA MINING RESEARCH , International Journal of Information Technology & Decision Making Vol. 5, No. 4 (2006) , pp 603-604 . - Top 10 challenging Problems in data mining (DM) : 10. Dealing with Non-Static, Unbalanced and Cost-Sensitive Data:

15. Conclusions : The presentation highlights on the most important 10 problems in data mining but in concise manner . The order of the sequence list does not reflect their level of important . 15

16. We must try to work hard to overcome these problems , because nowadays the one who owns the information he has the power . 16 Suggestions :

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