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Chapter One Introduction.

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Presentation on theme: "Chapter One Introduction."— Presentation transcript:

1 Chapter One Introduction

2 Chapter Overview Roles of data, information and knowledge
Background of data mining What is data mining? Main data mining objectives Data mining and other related disciplines Current state of data mining Promises and challenges A brief preview of data mining tool Weka

3 Data, Information and Knowledge
Data (D) Isolated factual recording of separate objects and events Enables the recording of the seen events Information (I) Fact of meaningful context represented by relationships between isolated data items Information enables the responding to the seen events Knowledge (K) Verified known information that is accommodated into the business process Enable the anticipation of the unseen events D I K

4 Data Mining: The Background
Computerisation of operations in commercial, governmental and scientific organisations has resulted in large volumes of operational data, e.g. Itemised telephone bills Bank statements Supermarket transactions Share prices Scientific experimental data sets Published web pages CCTV video footages ……

5 Data Mining: The Background
Facts: Storing the data is an operational necessity Storing the data has become easy and affordable Data acquisition is fully or partially automatic and fast Consequences: The speed of data comprehension does not match the speed of data acquisition Many commercial database management systems (DBMSs) are not equipped with data comprehension and analysis tools. We may be data rich, but information poor.

6 Data Mining: The Background
An intriguing quotable quote: “I know half the money I spend on advertising is wasted, but I can never find out which half!” Lord Leverhulme President of Unilever

7 Data Mining: What it is Knowledge discovery in databases (KDD) refers to the efficient process of searching through large volumes of raw data in databases to find potentially useful information that is implicitly embedded in the data. Data Mining is an integral step of KDD that discovers hidden patterns from an input data set. Useful information; leading to a course of action or an understanding of data Non-trivial implicit information; not the raw data, nor the result of a simple data summary Real life databases; not laboratory generated data sets Efficient novel discovery methods; expected to be scaled up and applied to large databases

8 Data Mining: Useful Information
Example 1 (A well-known example, not a joke): Customers who purchase beer are also likely (say 90%) to purchase nappies. Example 2 (May already be in practical use in credit card applications): If 20,000  Customer’s Salary  40,000 pounds and Customer has a house, then Customer is a safe customer.

9 Data Mining: Non-trivial Information
Putting the “search for information” into a spectrum: sophistication Low end of High end of Data retrieval Online analytic processing Data mining Retrieval of stored data Trivial data aggregation Written in standard SQL Interactive reporting on stored data Summarisation and drilling along different attributes Written in extended SQL Discovery of hidden and embedded patterns Discovery algorithms Written in programming language probably with the assistance of SQL

10 Data Mining: Real-life Databases
Characteristics of a real-life database The size may be extremely large The dimensionality can be very high Attributes can be of different data types Data quality can be very poor Data may exist in pieces and isolated in different systems Value distribution can be extremely skewed Database content can be dynamic and evolving Data may lack traditional record-based structure Data are available on second storage media

11 Data Mining: Efficient Algorithms
Discovering interesting patterns supported by given facts can be computationally hard because many discoveries are combinatorial problems. Trivial algorithms may take too long. A discovery algorithm is considered efficient if its execution time and memory requirement are comparable to those of sorting algorithms; otherwise, it is unlikely to scale up well enough to cope with data sets of large sizes. Efficient discovery algorithms may be hard to find. Using advanced hardware, optimising the implementation of the algorithms and developing approximate solutions can be viable alternative options.

12 Data Mining Objectives
Classification Using existing data to form a classification model and then using the model to assign an appropriate class label for a data record (e.g. safe vs. risky customers) Estimation Similar to classification but to assign a value to an output variable of a data record (e.g. estimated house value) Prediction Similar to classification and estimation, but more concerned with future outcome of the output (e.g. tomorrow’s weather) Description General description of data characteristics (e.g. customer profile)

13 Data Mining & Other Disciplines
Machine Learning (Artificial Intelligence) Statistics Inductive & deductive learning methods Data analysis theories methods and measures DATA MINING Fast storage structures & retrieval operations Data mining comes from three existing areas: statistics, machine learning and databases. Database Management

14 Data Mining: Current State
Many data mining algorithms have been developed or adapted Many data mining software tools have been built and are in use A cross-industry methodology has been formed Besides general solutions, more application-oriented data mining solutions are being developed More and more organisations are either doing their own data mining or hiring consultants to do the job Data mining has been extended to web mining and text mining

15 Data Mining: Current State
Some nuisances Mining cookies Spyware and miningware Intrusion to privacy Some serious problems “Big Brother is watching” Unfair advantages in trading practice e.g. high-frequency trading (HFT) Abuse of personal data Ethical concerns

16 Data Mining: Promises Areas of data mining application:
Finance and insurance Marketing and sales Medicine Agriculture Society, politics and economics Science Engineering Law enforcement Military and intelligence (classified)

17 Data Mining: Challenges Faced
Some difficult problems to solve Extremely large data sets Extremely high dimensionalities (curse of dimensions) Combinatorial problems and fast algorithms Meaningful evaluation of the patterns Discovery of changing and evolving patterns Integration of data mining techniques Comprehensibility of patterns Data pre-processing Mining non-standard complex data such as multimedia materials

18 Weka: A Brief Introduction
Overview Java tool set developed at Univ. of Waikato (NZ) Free to download and used by many A wide range of learning and data pre-processing methods and algorithms, with Java API Offering a GUI (Explorer) and a command-line (Simple CLI) interface to the tools Experimenter module to assist the evaluation of classification techniques KnowledgeFlow module to enable batch-processing style discovery and incremental mining Some visualisation facilities

19 Weka: A Brief Introduction
Weka Explorer For investigative interactive data mining with small size data sets Preprocess, Classify, Cluster, Associate, Select Attributes and Visualise pages

20 Weka: A Brief Introduction
Weka Simple CLI Weka facilities as Java classes Calling the Java functions as commands

21 Weka: A Brief Introduction
Weka Experimenter Comparing performances of different classification solutions on a collection of data sets

22 Weka: A Brief Introduction
Weka KnowledgeFlow Setting up a flow of knowledge discovery in a diagram Overview of the entire discovery project

23 Chapter Summary Importance of data in operation and importance of information and knowledge in decision-making Data rich does not mean information rich Data mining: automatic or semi automatic data understanding and decision support To classify, to estimate, to predict and to describe Data mining closely relates to database, statistics and machine learning Data mining: from technology towards application A lot of potential uses and a lot of challenges to face Weka: excellent tool to support teaching data mining

24 References Read Chapter 1 of Data Mining Techniques and Applications
Useful further references Han & Kamber, Chapter 1 Berry & Linoff, Chapter 1 (business-like) Kdnuggets:


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