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Classification and prediction in data mining

Classification and prediction in data mining,Descriptive Functions,Data Mining Techniques,Association,Classification ,Clustering,Prediction

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Classification and prediction in data mining

  1. 1. Er. Nawaraj Bhandari Data Warehouse/Data Mining Chapter 5:
  2. 2. Data Mining Definition and Task  There is a huge amount of data available in the Information Industry. This data is of no use until it is converted into useful information. It is necessary to analyze this huge amount of data and extract useful information from it. Data Mining is defined as extracting information from huge sets of data. In other words, we can say that data mining is the procedure of mining knowledge from data.
  3. 3. Data Mining Definition and Task  On the basis of the kind of data to be mined, there are two types of tasks that are performed by Data Mining:  Descriptive  Classification and Prediction
  4. 4. Descriptive Function The descriptive function deals with the general properties of data in the database. Here is the list of descriptive functions −  Class/Concept Description  Mining of Frequent Patterns  Mining of Associations  Mining of Correlations  Mining of Clusters
  5. 5. Classification and Prediction Classification is the process of finding a model that describes the data classes or concepts. The purpose is to be able to use this model to predict the class of objects whose class label is unknown. This derived model is based on the analysis of sets of training data. The derived model can be presented in the following forms  Classification (IF-THEN) Rules  Decision Trees  Mathematical Formulae  Neural Networks  Prediction is used to predict missing or unavailable numerical data values rather than class labels.
  6. 6. Data Mining Techniques 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.
  7. 7. 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 customer and increase sales.
  8. 8. Association
  9. 9. 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 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 application that “given all records of employees who left the company, predict who will probably leave the company in a future period.” In this case, we divide the records of employees into two groups that named “leave” and “stay”. And then we can ask our data mining software to classify the employees into separate groups.
  10. 10. Clustering Clustering is a data mining technique that makes meaningful or useful cluster of objects which have similar characteristics using 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 library as an example. In a library, there is a wide range of books in various topics available. The challenge is how to keep those books in a way that readers can take several books in a particular topic without hassle. By using 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 entire library.
  11. 11. Clustering
  12. 12. The prediction, as it name implied, is one of a data mining techniques that discovers relationship between independent variables and relationship between dependent variables. For instance, the prediction analysis technique can be used in sale to predict profit for the future if we consider 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. Prediction
  13. 13. Often used over longer-term data, sequential patterns are a useful method for identifying trends, or regular occurrences of similar events. For example, with customer data you can identify that customers buy a particular collection of products together at different times of the year. In a shopping basket application, you can use this information to automatically suggest that certain items be added to a basket based on their frequency and past purchasing history. Sequential Patterns
  14. 14. Decision tree is one of the most 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. Decision trees
  15. 15. Different types of data mining tools are available in the marketplace, each with their own strengths and weaknesses. These tools use artificial intelligence, machine learning and other techniques to extract data. Most data mining tools can be classified into one of three categories: traditional data mining tools, dashboards, and text-mining tools. Data Mining Tools
  16. 16.  Traditional data mining tools and techniques work with existing databases stored on enterprise servers or even local hard drives. They interpret the data stored there using pre-defined algorithms and queries written out in a database-specific programming language (macros) to reveal patterns in the data that would otherwise be invisible.  For example, a database of sales figures can easily display monthly sales trends simply by accessing the database’s built-in query and table system. A data mining tool installed to the server can then analyze those broad numbers to identify aspects affecting monthly sales that are not immediately apparent, and, most importantly, render that analysis into an easily-readable report form that makes those patterns explicit. Traditional Data Mining Tools
  17. 17. A more recent innovation in the world of data mining tools and techniques is the Dashboard. Dashboard is a piece of software that sits on an end-user’s desktop or tablet and reports real-time fluctuations in data as it flows into the database and is manipulated or sorted. Typically, historical data can also be accessed via the Dashboard, although the data mining of historic Dashboards are typically used by managers and other positions to track the effect of events and other influences on data streams in real time. One example is monitoring new picking policies in a warehouse as a company attempts to massage their logistical management of stock ”“ a Dashboard allows the company to see the effect of new policies immediately, quickly analyzing just a few hours of data to see if they getting the desired efficiency or not. Dashboards
  18. 18.  One of the newer innovations in data mining tools and techniques are text-mining applications. These tools take disparate forms of textual data ”“ word processing documents, plain text files, ‘flat’ text formats like PDF files or presentation files ”“ and mine them for patterns in the text.  This allows companies and users to use data mining tools and techniques without having to open each document in a separate application or perform cumbersome (and error-introducing) conversions on documents.  Text analysis has many possible techniques and applications. One popular one involves seeking out plagiarized or ‘copy pasted’ content. Text analysis data mining tools allow users to quickly scan huge amounts of text in different formats to identify identical strings and report back the odds that a particular piece of text was lifted from an existing text. Universities and colleges are using such tools more and more commonly to fight plagiarism in classrooms. Text Analysis
  19. 19.  Future Healthcare  Data mining holds great potential to improve health systems. It uses data and analytics to identify best practices that improve care and reduce costs. Researchers use data mining approaches like multi-dimensional databases, machine learning, soft computing, data visualization and statistics. Mining can be used to predict the volume of patients in every category. Processes are developed that make sure that the patients receive appropriate care at the right place and at the right time. Data mining can also help healthcare insurers to detect fraud and abuse. Data mining applications
  20. 20.  Market Basket Analysis  Market basket analysis is a modelling technique based upon a theory that if you buy a certain group of items you are more likely to buy another group of items. This technique may allow the retailer to understand the purchase behaviour of a buyer. This information may help the retailer to know the buyer’s needs and change the store’s layout accordingly. Using differential analysis comparison of results between different stores, between customers in different demographic groups can be done. Data mining applications
  21. 21.  Education  There is a new emerging field, called Educational Data Mining, concerns with developing methods that discover knowledge from data originating from educational Environments. The goals of EDM are identified as predicting students’ future learning behavior, studying the effects of educational support, and advancing scientific knowledge about learning. Data mining can be used by an institution to take accurate decisions and also to predict the results of the student. With the results the institution can focus on what to teach and how to teach. Learning pattern of the students can be captured and used to develop techniques to teach them. Data mining applications
  22. 22.  Manufacturing Engineering  Knowledge is the best asset a manufacturing enterprise would possess. Data mining tools can be very useful to discover patterns in complex manufacturing process. Data mining can be used in system-level designing to extract the relationships between product architecture, product portfolio, and customer needs data. It can also be used to predict the product development span time, cost, and dependencies among other tasks. Data mining applications
  23. 23.  CRM  Customer Relationship Management is all about acquiring and retaining customers, also improving customers’ loyalty and implementing customer focused strategies. To maintain a proper relationship with a customer a business need to collect data and analyse the information. This is where data mining plays its part. With data mining technologies the collected data can be used for analysis. Instead of being confused where to focus to retain customer, the seekers for the solution get filtered results. Data mining applications
  24. 24.  Fraud Detection  Billions of dollars have been lost to the action of frauds. Traditional methods of fraud detection are time consuming and complex. Data mining aids in providing meaningful patterns and turning data into information. Any information that is valid and useful is knowledge. A perfect fraud detection system should protect information of all the users. A supervised method includes collection of sample records. These records are classified fraudulent or non-fraudulent. A model is built using this data and the algorithm is made to identify whether the record is fraudulent or not. Data mining applications
  25. 25.  Fraud Detection  Billions of dollars have been lost to the action of frauds. Traditional methods of fraud detection are time consuming and complex. Data mining aids in providing meaningful patterns and turning data into information. Any information that is valid and useful is knowledge. A perfect fraud detection system should protect information of all the users. A supervised method includes collection of sample records. These records are classified fraudulent or non-fraudulent. A model is built using this data and the algorithm is made to identify whether the record is fraudulent or not. Data mining applications
  26. 26. References 1. Sam Anahory, Dennis Murray, “Data warehousing In the Real World”, Pearson Education. 2. Kimball, R. “The Data Warehouse Toolkit”, Wiley, 1996. 3. Teorey, T. J., “Database Modeling and Design: The Entity-Relationship Approach”, Morgan Kaufmann Publishers, Inc., 1990. 4. “An Overview of Data Warehousing and OLAP Technology”, S. Chaudhuri, Microsoft Research 5. “Data Warehousing with Oracle”, M. A. Shahzad 6. “Data Mining Concepts and Techniques”, Morgan Kaufmann J. Han, M Kamber Second Edition ISBN : 978-1-55860-901-3
  27. 27. ANY QUESTIONS?

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