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Introduction to data mining

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1 Introduction to data mining

2 Literature

3 Data mining in commerce
About 13 million customers per month contact the West Coast customer service call center of the Bank of America In the past, each caller would have listened to the same marketing advertisement, whether or not it was relevant to the caller’s interests. Chris Kelly, vice president and director of database marketing: “rather than pitch the product of the week, we want to be as relevant as possible to each customer” Thus, based on individual customer profiles, the customer can be informed of new products that may be of greatest interest. Data mining helps to identify the type of marketing approach for a particular customer, based on the customer’s individual profile.

4 Recommendation systems

5 Why mine data – commercial viewpoint
Lots of data is being collected Web data, e-commerce purchases at department/grocery stores Bank/Credit Card transactions Computers have become cheaper and more powerful Competitive pressure is strong Provide better, customized services R. Grossman, C. Kamath, V. Kumar, “Data Mining for Scientific and Engineering Applications”

6 Why mine data – scientific viewpoint
Data collected and stored at enormous speeds (GB/hour) remote sensors on a satellite telescopes scanning the skies microarrays generating gene expression data scientific simulations generating terabytes of data Traditional techniques infeasible for raw data Data mining may help scientists in classifying and segmenting data in hypothesis formation R. Grossman, C. Kamath, V. Kumar, “Data Mining for Scientific and Engineering Applications”

7 Data mining in bioinformatics
Brain tumors represent the most deadly cancer among children Gene expression database for pediatric brain tumors was built, in an effort to develop more effective treatment.

8 Clearly, a lot of data is being collected.
However, what is being learned from all this data? What knowledge are we gaining from all this information? “we are drowning in information but starved for knowledge” The problem today is not that there is not enough data. Rather, the problem is that there are not enough trained human analysts available who are skilled at translating all of this data into knowledge.

9 Data mining is the process of discovering meaningful new correlations, patterns and trends by sifting through large amounts of data stored in repositories, using pattern recognition technologies as well as statistical an mathematical techniques. ( Data mining is an interdisciplinary field bringing togther techniques from machine learning, pattern recognition, statistics, databases, and visualization to address the issue of information extraction from large data bases. (Peter Cabena, Pablo Hadjinian, Rolf Stadler, JaapVerhees, and Alessandro Zanasi, Discovering Data Mining: From Concept to Implementation, Prentice Hall, Upper Saddle River, NJ, 1998.) this is where data mining comes into play - there exist many many many definitions of DM, I picked just two of them

10 The growth in this field has been fueled by several factors:
growth in data collection storing of the data in data warehouses availability of increased access to data from Web competitive pressure to increase market share development of data mining software suites tremendous growth in computing power and storage capacity

11 Need for human direction of DM
Don’t believe software vendors advertising their analytical software as being plug-and-play out-of-the-box application providing solutions without the need of human interaction! Data mining is not a product that can be bought, it is a discipline that must be mastered!

12 It is easy to do data mining badly. Software always gives some result.
A little knowledge is especially dangerous e.g. analysis carried out on unpreprocessed data can lead to errorneous conclusions, the models can be way off if deployed, the errors can lead to very expensive failures The costly errors stem from the black-box approach.

13 Data maning trap If we try hard enough, we always find some patterns.
However, they may be just a matter of chance. They don’t have to be characteristic for process that generates the data. Derogatory sport definition of data mining: Data mining means sorting through a huge volume of data, extracting decision rules that seem to favor one team over another, but without regard to whether or not there is any cause-and-effect relationship. Data mining is the equivalent of sitting a huge number of monkeys down at keyboards, and then reporting on the monkeys who happened to type actual words. Derogatory – opovržlivý „Data mining“ ve sportovním odvětví znamená toto:

14 Instead, apply a “white-box” methodology.
i.e. understand algorithms and statistical model structures underlying a software The white-box approach is the reason why you are attending this lecture (apart from the fact, that the lecture is compulsory).

15 Data mining as a process
One of the fallacies associated with DM is that DM represents an isolated set of tools Instead, DM should be viewed as a process The process is standardized – CRISP-DM framework ( Cross-Industry Standard Process for Data Mining developed in 1996 by analysts from DaimlerChrysler, SPSS, and NCR provides a nonproprietary and freely available standard process for fitting data mining into the general problem-solving strategy of a business or research unit fallacy – mylna predstava, blud NCR Corporation, formerly National Cash Register - a technology company specializing in products for the retail, financial, travel, healthcare, food service, entertainment, gaming and public sector industries

16 CRISP-DM starts here - life cycle consisting of six phases
the phase sequence is adaptive (e.g. depending on the behavior and characteristics of the model, we may have to return to the data preparation phase for further refinement before moving forward to the model evaluation phase)

17 Business understanding phase Data understanding phase
Formulate the project objectives and requirements Data understanding phase collect the data use EDA (exploratory data analysis) to familiarize yourself with the data evaluate the quality of the data Data preparation phase prepare from the initial raw data the final data set. This phase is very labor intensive. select the cases and variables you want to analyze perform transformation of variables, if needed clean the raw data so they are ready for modelling tools

18 Modeling phase Evaluation phase
select and apply appropriate modeling techniques calibrate model settings to optimize results often, several different techniques may be used if necessary, loop back to the data preparation phase to bring the form of the data into line with the specific requirements of a particular data mining technique Evaluation phase evaluate models for quality and effectivness establish whether some important facet of the business or research problem has not been accounted for sufficiently - enunciate - formulovat

19 Deployment phase make use of the models created
examples of deployment: report implement a parallel DM process in another department

20 CRISP-DM example Investigated patterns in the warranty claims
for DaimlerChrysler automobiles Business understanding Objectives: reduce costs associated with warranty claims and improve customer satisfaction Specific business problems can be formulated: Are there interdependencies among warranty claims? Are past warranty claims associated with similar claims in the future? Jochen Hipp and Guido Lindner, Analyzing warranty claims of automobiles: an application description following the CRISP–DM data mining process, in Proceedings of the 5th International Computer Science Conference (ICSC ’99), pp. 31–40, Hong Kong, December 13–15, 1999

21 Data understanding use of DaimlerChrysler’s Quality Information System (QUIS) it contains information on over 7 million vehicles and is about 40 gigabytes in size QUIS contains production details about how and where a particular vehicle was constructed + warranty claim information researchers stressed the fact that the database was entirely unintelligible to domain nonexperts experts from different departments had to be located and consulted, a task that turned out to be rather costly - unintelligible - nesrozumitelny

22 Data preparation the QUIS DB did not contain all information needed for the modelling purposes e.g. the variable “number of days from selling date until first claim” had to be derived from the appropriate date attributes researchers then turned to DM software where they ran into a common roadblock: data format requirements varied from algorithm to algorithm result was further exhaustive preprocessing of the data researchers mention that the data preparation phase took much longer than they had planned - unintelligible - nesrozumitelny

23 Modeling to investigate dependencies, researchers used
Bayesian networks Association rules mining the details of the results are confidential, but we can get general idea of dependencies uncovered by models particular combination of construction specifications doubles the probability of encountering an automobile electrical cable problem - unintelligible - nesrozumitelny

24 Evaluation The researchers were disappointed that association rules models were found to be lacking in effectiveness and to fall short of the objectives set for them in the business understanding phase “In fact, we did not find any rule that our domain experts would judge as interesting.” To account for this, the researchers point to the “legacy” structure of the database, for which automobile parts were categorized by garages and factories for historic or technical reasons and not designed for data mining. They suggest redesigning the database to make it more amenable to knowledge discovery. - unintelligible - nesrozumitelny

25 Deployment It was a pilot project, without intention to deploy any large-scale models from the first iteration. Product: report describing lessons learned from this project e.g. change of the structure of the database (new variables, different categorization of automobile parts) - unintelligible - nesrozumitelny

26 Lessons learned uncovering hidden nuggets of knowledge in databases is a rocky road intense human participation and supervision is required at every stage of the data mining process there is no guarantee of positive results

27 Connection to other fields
Machine learning Pattern recognition Vizualization Data Mining Database systems Statistics

28 Machine learning A subfield of artificial intelligence.
Discipline that is concerned with the design and development of algorithms that allow computers to evolve behavior based on experience. experience – empirical data, such as from sensors or databases evolve behavior – usually through search of patterns in data similar goal as DM, DM uses algorithms from ML

29 Pattern recognition Problem of searching patterns - a fundamental one, long and successful history. For instance, the extensive astronomical observations of Tycho Brahe in the 16th century allowed Johannes Kepler to discover the empirical laws of planetary motion, which in turn provided a springboard for the development of classical mechanics.

30 Pattern recognition automatic discovery of regularities in data through the use of computer algorithms and with the use of these regularities to take actions such as classifying the data into different categories

31 Pattern recognition data patterns
if train has 2 wagons, it goes to the left

32 More real patterns face detection

33 Connection to other fields
Machine learning Pattern recognition Vizualization Data Mining Database systems Statistics

34 Iris Sample Data Set Many of the exploratory data techniques are illustrated with the Fisher’s Iris Plant data set. From the statistician Douglas Fisher, mid-1930s Can be obtained from the UCI Machine Learning Repository based on WEKA tutorial

35 Contains flower dimension measurements on 50 samples of each species.
iris setosa iris versicolor iris virginica Contains flower dimension measurements on 50 samples of each species. Let’s examine the origins of Fisher’s iris dataset. During the 1930’s, botanist Edgar Anderson traveled to the Gaspe’ Peninsula to study irises. His work resulted in the creation of a dataset (as we now term it) containing measurements of flower petal and sepal dimensions for each of three different species of iris. The dataset contains 50 samples of each of the three species, for a total of 150 samples. Anderson’s dataset was then used by Ronald Fisher in a landmark study describing a means of classifying an iris of unknown species based on flower dimension measurements. Fisher, R.A. (1936). "The Use of Multiple Measurements in Taxonomic Problems". Annals of Eugenics 7: 179–188,

36 These dimensions were measured: sepal (kališní lístek) length
sepal width petal (korunní lístek) length petal width Measurements on these iris species: setosa versicolor virginica Anderson performed measurements on four flower dimensions: sepal length, sepal width, petal length, and petal width. He performed these measurements on the three iris species: setosa, versicolor, and virginica. Data mining terminology The four iris dimensions are termed attributes, input attributes, features The three iris species are termed classes, output attributes Each example of an iris is termed a sample, instance, object, data point based on WEKA tutorial

37 Sample Class Nominal Numerical
Here is a segment of the Fisher’s dataset spreadsheet that you downloaded showing the four input attributes and one output attribute, or class, for five of the 150 instances, or samples, of iris. For this dataset, the input attributes are numerical attributes, meaning that the attributes are given as real numbers, in this case in centimeters. The output attribute is a nominal attribute, in other words, a name for a particular species of iris. Nominal Numerical based on WEKA tutorial

38 Statistics statistical analysis Exploratory Data Analysis (EDA)
summary statistics (mean, median, standard deviation) Exploratory Data Analysis (EDA) A preliminary exploration of the data to better understand its characteristics. Created by statistician John Tukey A nice online introduction can be found in Chapter 1 of the NIST Engineering Statistics Handbook

39 EDA Helps to select the right tool for preprocessing or analysis
People can recognize patterns not captured by data analysis tools In EDA, as originally defined by Tukey The focus was on visualization Clustering and anomaly detection were viewed as exploratory techniques In data mining, clustering and anomaly detection are major areas of interest, and not thought of as just exploratory Human makes and validates hypotheses While in DM computer makes and validates hypotheses

40 versicolor setosa virginica
As it turns out for this histogram, dark blue is setosa, red is versicolor, and bright blue is virginica. The histogram shows that there are, for example, 16 samples in the lower histogram bin for sepal length, all of which are setosa irises, and shows that there are 7 samples in the highest bin, all of which are virginica. based on WEKA tutorial

41 versicolor virginica setosa
By comparing the histograms for all of the input attributes, we can begin to get a sense of how the four input attributes vary with different iris species. For example, it appears that iris setosa tends to have relatively small sepal length, petal length, and petal width, but relatively large sepal width. These are the types of patterns that data mining algorithms use to perform classification and other functions. Notice also that the Species histogram verifies that we have 50 of each iris species in our dataset. based on WEKA tutorial

42 sepal width sepal length Here we see a plot of the dataset.
Sepal length is displayed on the x-axis, and sepal width on the y-axis. The plot screen also shows us which colors are associated with which classes on the various Weka screens. sepal length based on WEKA tutorial

43 Connection to other fields
Machine learning Pattern recognition Vizualization Data Mining Database systems Statistics

44 Visualization Can reveal hypotheses based on WEKA tutorial

45 Connection to other fields
Machine learning Pattern recognition Vizualization Data Mining Database systems Statistics

46 Data warehouse A data warehouse is a repository of an organization's electronically stored data. Data warehouses are designed to facilitate reporting and analysis. Technology: relational database system multidimensional database system

47 Data warehousing process of constructing and using data warehouse
Data warehousing is the coordinated, periodic copying of data from various sources, both inside and outside the enterprise, into an environment optimized for analytical and informational processing.

48 data warehousing includes business intelligence tools
-ERP - Enterprise resource planning -CRM - Customer relationship management data warehousing includes business intelligence tools tools to extract, transform, and load data tools to manage and retrieve metadata

49 Business intelligence tools
a type of application software designed to report, analyze and present data they include reporting and querying software “Tell me what happened.” tools that extract, sort, summarize, and present selected data OLAP (On-Line Analytical Processing ) “Tell me what happened and why.” data mining “Tell me what might happened.” (predict) “Tell me something interesting.” (relationships)

50 OLAP Query and report data is typically presented in row after row of two-dimensional data. OLAP: “Tell me what happened and why.” To support this type of processing, OLAP operates against multidimensional databases.

51 Example: Iris data We show how the attributes, petal length, petal width, and species type can be converted to a multidimensional array First, we discretized the petal width and length to have categorical values: low, medium, and high We get the following table - note the count attribute

52 Length Each unique tuple of petal width, petal length, and species type identifies one element of the array. This element is assigned the corresponding count value. The figure illustrates the result. -All non-specified tuples are 0.

53 Slices of the multidimensional array are shown by the following cross-tabulations
Setosa Versicolor Virginica

54 Creating a Multidimensional Array
Two key steps in converting tabular data into a multidimensional array. identify which attributes are to be the dimensions and which attribute is to be the target attribute whose values appear as entries in the multidimensional array. The attributes used as dimensions must have discrete values The target value is typically a count or continuous value find the value of each entry in the multidimensional array by summing the values (of the target attribute) or count of all objects that have the attribute values corresponding to that entry.

55 OLAP Operations: Data Cube
The key operation of an OLAP is the formation of a data cube. A data cube is a multidimensional representation of data, together with all possible aggregates.

56 By all possible aggregates, we mean the aggregates that result by selecting a proper subset of the dimensions and summing over all remaining dimensions. For example, if we choose the species type dimension of the Iris data and sum over all other dimensions, the result will be a one-dimensional entry with three entries, each of which gives the number of flowers of each type. Length

57 Data Cube Example Consider a data set that records the sales of products at a number of company stores at various dates. This data can be represented as a 3 dimensional array There are 3 two-dimensional aggregates (3 choose 2 ), 3 one-dimensional aggregates, and 1 zero-dimensional aggregate (the overall total)

58 The following figure table shows one of the two dimensional aggregates, along with two of the one-dimensional aggregates, and the overall total

59 OLAP Operations Various operations are defined on the data cube:
Slicing/Dicing - selecting a group/subgroup of cells from the entire multidimensional array by specifying a specific value for one or more dimensions. Roll-up and Drill-down - granularity Dicing - selecting a subset of cells by specifying a range of attribute values. Slicing - selecting a group of cells from the entire multidimensional array by specifying a specific value for one or more dimensions.

60 The End

61 OLAP Operations: Roll-up and Drill-down
Attribute values often have a hierarchical structure. Each date is associated with a year, month, and week. A location is associated with a continent, country, state (province, etc.), and city. Products can be divided into various categories, such as clothing, electronics, and furniture. Note that these categories often nest and form a tree or lattice A year contains months which contains day A country contains a state which contains a city

62 OLAP Operations: Roll-up and Drill-down
This hierarchical structure gives rise to the roll-up and drill-down operations. For sales data, we can aggregate (roll up) the sales across all the dates in a month. Conversely, given a view of the data where the time dimension is broken into months, we could split the monthly sales totals (drill down) into daily sales totals.

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