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Week-1-Introduction to Data Mining.pptx

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Week-1-Introduction to Data Mining.pptx

  1. 1. By Dr.T.GopiKrishna Assistant Professor Dept of Computer Sciene and Engineering
  2. 2. What is Data Mining? Extracting and ‘Mining’ knowledge from large amounts of data(Big data). (Or) Non-trivial extraction of implicit, previously unknown and potentially useful information from data. (or) Exploration & analysis, by automatic or semi-automatic means, of large quantities of data in order to discover meaningful patterns. “Gold Mining from rock or sand” is same as “Knowledge mining from data” Other terms for Data Mining: o Knowledge Mining o Knowledge Extraction o Pattern Analysis o Data Archaeology Data Mining is not same as KDD (Knowledge Discovery from Data) Data Mining is a step in KDD
  3. 3. Why Mine Data? Commercial Viewpoint
  4. 4. Why Mine Data? Scientific Viewpoint
  5. 5.  There is often information “hidden” in the data that is not readily evident.  Human analysts may take weeks to discover useful information.  Much of the data is never analyzed at all • Huge Volume of data • Major Sources of Abundant data: - Business – Web, E-commerce, Transactions, Stocks - Science – Remote Sensing, Bio informatics, Scientific Simulation - Society and Everyone – News, Digital Cameras, You Tube • Need for turning data into knowledge – Drowning in data, but starving for knowledge • Applications that use data mining: - Market Analysis - Fraud Detection - Customer Retention - Production Control - Scientific Exploration • Data rich and information poor situation
  6. 6. Machine Learning/ Pattern Recognition Statistics/ AI Data Mining Database systems
  7. 7.  Mostly reads  Queries are long and complex  Gb - Tb of data  History  Lots of scans  Summarized, reconciled data  Hundreds of users (e.g., decision- makers, analysts) Data Warehouse:-Data spread in several databases – physically located at numerous sites Data warehouse – repository of multiple DBs in single schema; resides at single site.
  8. 8.  Machine learning is a field of artificial intelligence that uses statistical techniques to give computer systems the ability to "learn"  Machine learning explores the study and construction of algorithms that can learn from and make predictions on data.  Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on prediction- making through the use of computers.
  9. 9. “Machine Learning is the science of getting computers to learn and act like humans do, and improve their learning over time in autonomous fashion, by feeding them data and information in the form of observations and real-world interactions.”
  10. 10.  Statistics – “Learning from Data” or “Turning data into information”.  Data – Crude Information – Does not makes sense – What we capture & store e.g. customer data, store data, demographical data, geographical data  Information – relates items of data – relevant to the decision problem e.g. X lives in Z; S is Y years old; X and S moved; W has money in Z  Facts – Information becomes facts when data can support it  Knowledge – What we know or infer – relates items of information e.g. a quantity Q of product A is used in region Z; customers of class L use N% of C in period D
  11. 11.  Databases  Data Warehousing  Statistics  Machine Learning  Information Retrieval  Image and Signal Processing  Pattern Recognition  Neural Networks  Data Visualization  Spatial / Temporal Data Analysis
  12. 12. Database-oriented data sets and applications o Relational database, data warehouse, transactional database Advanced data sets and advanced applications o Data streams and sensor data o Time-series data, temporal data, sequence data (incl. bio- sequences) o Structure data, graphs, social networks and multi-linked data  Object-relational databases o Heterogeneous databases and legacy databases o Spatial data and spatiotemporal data o Multimedia database o Text databases o The World-Wide Web
  13. 13.  Prediction Methods Use some variables to predict unknown or future values of other variables.  Description Methods Find human-interpretable patterns that describe the data.
  14. 14. Classification [Predictive] Clustering [Descriptive] Association Rule Discovery [Descriptive] Sequential Pattern Discovery [Descriptive] Regression [Predictive] Deviation Detection [Predictive]
  15. 15.  Data mining uncovers this in-depth business intelligence by using advanced analytical and modelling techniques.  With data mining, you can ask far more sophisticated questions of your data than you can with conventional querying methods.
  16. 16. Data mining is simply the acquisition of information that is already present in your CRM (Customer Relationship Management System) that is intended to be utilized for marketing, customer service, customer informative services and similar applications.
  17. 17.  Data mining tools ease and automate the process of discovering this kind of information from large stores of data.  Data mining can identify patterns in company data, for example, in records of supermarket purchases. If, for example, customers buy product A and product B, which product C are they most likely to buy as well? Accurate answers to questions like these are invaluable aids to marketing strategies.  Data mining can identify the characteristics of a known group of customers, for example, those who have a proven record as poor credit risks.
  18. 18.  Relational Databases: Consists of Database (inter related data) and set of software programs to manage and access data. Collection of tables Each table has a set of attributes (columns / fields) and large set of tuples (records or rows) .  Transactional Databases: Consists of a file with records where each record is a transaction. Each transaction has a unique transaction ID and list of items that make up transactions.  Object-Relational Databases:  Temporal Databases, Sequence Databases and Time-Series Databases  Spatial Databases and Spatiotemporal Databases:  Text Databases and Multimedia Databases:  Heterogeneous Databases and Legacy Databases:
  19. 19. Stages of Data Mining Process
  20. 20. KDD Process
  21. 21. Brief explanation of data mining stages
  22. 22. There are several major data mining techniques have been developing and using in data mining projects recently including  association,  classification,  clustering,  prediction,  sequential patterns and  decision tree.
  23. 23. Data Mining Techniques(Association)  Association is one of the best-known data mining technique. In association, a pattern is discovered based on a relationship between items in the same transaction.  That’s is the reason why association technique is also known as relation technique. The association technique is used in market basket analysis to identify a set of products that customers frequently purchase together.  Retailers are using association technique to research customer’s buying habits. Based on historical sale data, retailers might find out that customers always buy crisps when they buy beers, and, therefore, they can put beers and crisps next to each other to save time for the customer and increase sales.
  24. 24. Classification  Classification is a classic data mining technique based on machine learning. Basically, classification is used to classify each item in a set of data into one of a predefined set of classes or groups.  Classification method makes use of mathematical techniques such as decision trees, linear programming, neural network, and statistics.  In classification, we develop the software that can learn how to classify the data items into groups. For example, we can apply classification in the application that “given all records of employees who left the company, predict who will probably leave the company in a future period.”
  25. 25. Clustering  Clustering is a data mining technique that makes a meaningful or useful cluster of objects which have similar characteristics using the automatic technique.  The clustering technique defines the classes and puts objects in each class, while in the classification techniques, objects are assigned into predefined classes.  To make the concept clearer, we can take book management in the library as an example. In a library, there is a wide range of books on various topics available.  The challenge is how to keep those books in a way that readers can take several books on a particular topic without hassle.  By using the clustering technique, we can keep books that have some kinds of similarities in one cluster or one shelf and label it with a meaningful name. If readers want to grab books in that topic, they would only have to go to that shelf instead of looking for the entire library.
  26. 26. Prediction  The prediction, as its name implied, is one of a data mining techniques that discovers the relationship between independent variables and relationship between dependent and independent variables.  For instance, the prediction analysis technique can be used in the sale to predict profit for the future if we consider the sale is an independent variable, profit could be a dependent variable.  Then based on the historical sale and profit data, we can draw a fitted regression curve that is used for profit prediction.
  27. 27. Sequential Patterns  Sequential patterns analysis is one of data mining technique that seeks to discover or identify similar patterns, regular events or trends in transaction data over a business period.  In sales, with historical transaction data, businesses can identify a set of items that customers buy together different times in a year.  Then businesses can use this information to recommend customers buy it with better deals based on their purchasing frequency in the past.
  28. 28. Decision trees The A decision tree is one of the most commonly used data mining techniques because its model is easy to understand for users. In decision tree technique, the root of the decision tree is a simple question or condition that has multiple answers. Each answer then leads to a set of questions or conditions that help us determine the data so that we can make the final decision based on it. For example, We use the following decision tree to determine whether or not to play tennis:
  29. 29. Knowledge Representation Knowledge representation is the presentation of knowledge to the user for visualization in terms of trees, tables, rules graphs, charts, matrices, etc. For Example: Histograms
  30. 30. Histograms •Histogram provides the representation of a distribution of values of a single attribute. •It consists of a set of rectangles, that reflects the counts or frequencies of the classes present in the given data. Example: Histogram of an electricity bill generated for 4 months, as shown in diagram given below.
  31. 31. Data Visualization It deals with the representation of data in a graphical or pictorial format. Patterns in the data are marked easily by using the data visualization technique. Pixel- oriented visualization technique In pixel based visualization techniques, there are separate sub-windows for the value of each attribute and it is represented by one colored pixel.
  32. 32. Pixel- oriented visualization technique •The color mapping of the pixel is decided on the basis of data characteristics and visualization tasks.
  33. 33. Geometric projection visualization technique i. Scatter-plot matrices It consists of scatter plots of all possible pairs of variables in a dataset. ii. Hyper slice It is an extension to scatter-plot matrices. They represent multi- dimensional function as a matrix of orthogonal two dimensional slices. iii. Parallel co-ordinates T he parallel vertical lines which are separated defines the axes. A point in the Cartesian coordinates corresponds to a polyline in parallel coordinates. 3. Icon-based visualization techniques Icon-based visualization techniques are also known as iconic display techniques. Each multidimensional data item is mapped to an icon. This technique allows visualization of large amount of data. The most commonly used technique is Chernoff faces.
  34. 34. Chernoff faces For example: The face width, the length of the mouth and the length of nose, etc. as shown in the following diagram.
  35. 35. Visualization techniques Hierarchical visualization techniques  Hierarchical visualization techniques are used for partitioning of all dimensions in to subset.  These subsets are visualized in hierarchical manner.
  36. 36. Some of the visualization techniques are: i. Dimensional stacking In dimension stacking, n-dimensional attribute space is partitioned in 2-dimensional subspaces. Attribute values are partitioned into various classes. Each element is two dimensional space in the form of xy plot. Helps to mark the important attributes and are used on the outer level. ii. Mosaic plotMosaic plot gives the graphical representation of successive decompositions. Rectangles are used to represent the count of categorical data and at every stage, rectangles are split parallel.
  37. 37. Tree maps visualization  Techniques are well suited for displaying large amount of hierarchical structured data.  The visualization space is divided into the multiple rectangles that are ordered, according to a quantitative variable.  The levels in the hierarchy are seen as rectangles containing the other rectangle.  Each set of rectangles on the same level in the hierarchy represents a category, a column or an expression in a data set.  Visualization complex data and relations  This technique is used to visualize non-numeric data. For example: text, pictures, blog entries and product reviews.
  38. 38. Expert systems Rely on domain experts for decision making - using their knowledge intuition o Time consuming, costly, error prone, biased So the solution is to use Data Mining tools – performs data analysis, - finds data patterns
  39. 39. Knowledge Base: Domain knowledge is used to guide search – used to evaluate interestingness of patterns. Includes concept hierarchies, user benefits, thresholds, metadata Database / Data warehouse Server: Responsible for fetching relevant data based on data mining request. Data Mining Engine: Consists of modules for characterization, association, correlation analysis, classification, cluster analysis, prediction, outlier analysis and evolution analysis. Pattern Evaluation Module: Interacts with data mining modules. Focuses the search towards interesting patterns. Pattern evaluation module may be integrated with mining module to confine the search. User Interface: Communicates between users and data mining system Specifies data mining query – to focus search Uses intermediate data mining results to perform exploratory
  40. 40. Major Issues in Data Mining: Mining Methodology Issues: o Mining different kinds of knowledge in databases. o Incorporation of background knowledge o Handling noisy or incomplete data o Pattern Evaluation – Interestingness Problem User Interaction Issues: o Interactive mining of knowledge at multiple levels of abstraction o Data mining query languages and ad-hoc data mining. o Presentation and visualization of data mining results. Performance Issues: o Efficiency and Scalability of Data Mining Algorithms. o Parallel, distributed and incremental mining algorithms. Issues related to diversity of data types: o Handling of relational and complex types of data. o Mining information from heterogeneous databases and global I nformation systems.
  41. 41. Review Questions 1. What motivated Data Mining? Why is it important? 2. What is Data Mining? 3. Explain the steps in the Knowledge Discovery Process. 4. Detail on the Architecture of Data Mining Systems with a suitable diagram. 5. Explain about various Data Mining functionalities 6. Discuss about the major issues in data mining.