Data mining chapter 2 input concepts instances and attributes
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Data Mining Chapter 2 Input: Concepts, Instances, and Attributes. Kirk Scott. Hopefully the idea of instances and attributes is clear Assuming there is something in the data to be mined, either this is the concept, or the concept is inherent in this

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Data Mining Chapter 2 Input: Concepts, Instances, and Attributes

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Data MiningChapter 2Input: Concepts, Instances, and Attributes

Kirk Scott

  • Hopefully the idea of instances and attributes is clear

  • Assuming there is something in the data to be mined, either this is the concept, or the concept is inherent in this

  • Earlier data mining was defined as finding a structural representation

  • Essentially the same idea is now expressed as finding a concept description

Concept Description

  • The concept description needs to be:

  • Intelligible

    • It can be understood, discussed, disputed

  • Operation

    • It can be applied to actual examples

2.1 What’s a Concept?

Reiteration of Types of Discovery

  • Classification

    • Prediction

  • Clustering

    • Outliers

  • Association

  • Each of these is a concept

  • Successful accomplishment of these for a data set is a concept description

Recall Examples

  • Weather, contact lenses, iris, labor contracts

  • All were essentially classification problems

  • In general, the assumption is that classes are mutually exclusive

  • In complicated problems, data sets may be classified in multiple ways

  • This means individual instances can be “multilabeld”

Supervised Learning

  • Classification learning is supervised

  • There is a training set

  • A structural representation is derived by examining a set of instances where the classification is known

  • How to test this?

  • Apply the results to another data set with known classifications

Association Rules

  • In any given data set there can be many association rules

  • The total may approach n(n – 1) / 2 for n attributes

  • The book doesn’t use the terms support and confidence, but it discusses these concepts

  • These terms will be introduced

Support for Association Rules

  • Let an association rule X = (x1, x2, …, xi)y be given in a data set with m instances

  • The support for Xy is the count of the number of instances where the combination of x values, X, occurs in the data set, divided by m

  • In other words, the association rule may be interesting if it occurs frequently enough

Confidence for Association Rules

  • Confidence here is based on the statistical use of the term

  • The confidence for Xy is the count of the number of occurrences in the data set where this relationship holds true divided by the number of occurrences of X overall

  • The book describes this idea as accuracy

  • In other words, the association is interesting the more likely it is that X does determine y


  • We haven’t gotten the details yet, but this is an interesting data mining problem

  • Given a data set without predefined classes, is it possible to determine classes that the instances fall into?

  • Having determined the classes, can you then classify future instances into them?

  • Outliers are instances that you can definitely say do not fall into any of the classes

Numerical Prediction

  • This is a variation on classification

  • Given n attribute values, determine the (n + 1)st attributed value

  • Recall the CPU performance problem

  • It would be a simple matter to dream up sample data where the weather data predicted how long you would play rather than a simple yes or no

  • (The book does so)

2.2 What’s in an Example?

  • The authors are trying to present some important ideas

  • In case their presentation isn’t clear, I present it here in a slightly different way

  • The basic premise goes back to this question:

  • What form does a data set have to be in in order to apply data mining techniques to it?

Data Sets Should Be Tabluar

  • The simple answer based on the examples presented so far:

  • The data has to be in tabular form, instances with attributes

  • The remainder of the discussion will revolve around questions related to normalization in db

Not All Data is Naturally Tabular

  • Some data is not most naturally represented in tabular form

  • Consider OO db’s, where the natural representation is tree-like

  • How should such a representation be converted to tabular form that is amenable to data mining?

Correctly Normalized Data May Fall into Multiple Tables

  • You might also have data which naturally falls into >1 table

  • Or, you might have data (god forbid) that has been normalized into >1 table

  • How do you make it conform to the single table model (instances with attributes) for data mining?

  • Tree-like data and multi-table data may be related questions

  • It would not be surprising to find that a conversion of a tree to a table resulted in >1 table


  • The situation goes against the grain of correct database design

  • The classification, association, and clustering you intend to do may cross db entity boundaries

  • The fact that you want to do mining on a single tabular representation of the data means you have to denormalize

  • In short, you combine multiple tables back into one table

  • The end result is the monstrosity that is railed against in normalization theory:

  • The monolithic, one-table db

The Book’s Family Examples

  • Family relationships are typically viewed in tree-like form

  • The book considers a family tree and the relationship “is a sister of”

  • The factors for inferring sisterhood:

  • Two people, one female

  • The same (or at least one common) parents for both people

Two People in the Same Table

  • Suppose you want to do this in tabular form

  • You end up with the two people who might be in a sisterhood relationship in the same table

  • These occurrences of people are matched with a classification, yes or no

  • Recall that according to normalization, a truly one-to-one relationship can be stored in a single table

  • Pairings of all people would result in lots of instances/rows where the classification was simply no

  • This isn’t too convenient

  • In theory, you might restrict your attention only to those rows where the classification was yes

  • This restriction is known as the “closed world assumption” in data mining

  • Unfortunately, it is hardly ever the case that you have a problem where this kind of simplifying assumption applies

  • You have to deal with all cases

Two People with Attributes in the Same Table

  • Suppose the two people are only listed by name in the table, without parent information

  • The classification might be correct, but this is of no help

  • There are no attributes to infer sisterhood from

  • The table has to include attributes about the two people, namely parent information

The Connection with Normalization

  • There is a problem with denormalized data mining which is completely analogous to the normalization problem

  • Suppose you have two people in the same instance (the same row) with their attributes

  • By definition, you will have stray dependencies

  • The Person identifiers determine the attributes values

  • So far we’ve considered classification

  • However, what would happen if you mined for associations?

  • The algorithm would find the perfectly true, but already known associations between the pk identifiers of the people and their attribute fields

  • This is not helpful

  • It’s a waste of effort

Recursive Relationships

  • Recall the monarch and product-assembly examples from db

  • These give tables in recursive relationships with themselves or others

  • In terms of the book’s example, how do you deal with parenthood when there is a potentially unlimited sequence of ancestors?

  • In general, the answer is that you would need recursive rules

  • Mining recursive rules is a step beyond classification, association, etc.

  • The good news is that this topic will not be covered further

  • It’s simply of interest to know that such problems can arise

One-to-Many Relationships

  • A denormalized table might be the result joining two tables in a pk-fk relationship

  • If the classification is on the “one” side of the relationship, then you have multiple instances in the table which are not independent

  • In data mining this is called a multi-instance situation

  • The multiple instances belonging to one classification together actually form one example of the concept under consideration in such a problem

  • Data mining algorithms have been developed to handle cases like these

  • They will be presented with the other algorithms later

Summary of 2.2

  • The fundamental practical idea here is that data sets have to be manipulated into a form that’s suitable for mining

  • This is the input side of data mining

  • The reality is that denormalized tables may be required

  • Data mining can be facetiously be referred to as file mining since the required form does not necessarily agree with db theory

  • The situation can be restated in this way:

  • Assemble the query results first; then mine them

  • This leads to an open question:

  • Would it be possible to develop a data mining system that could encompass >1 table, crawling through the pk-fk relationships like a query, finding assocations?

2.3 What’s in an Attribute?

  • This subsection falls into two parts:

  • 1. Some ideas that go back to db design and normalization questions

  • 2. Some ideas having to do with data type

Design and Normalization

  • You could include different kinds (subtypes) of entities in the same table

  • To make this work you would have to include all of the fields of all of the kinds of entities

  • The fields that didn’t apply to a particular instance would be null

  • The book uses transportation vehicles as an example: ships and trucks

  • You could also have fields in a table that depend on each other (ack)

  • The book gives married T/F and spouse’s name as examples

  • Again, you can handle this with null values

Data Types

  • The simplest distinction is numeric vs. categorical

  • Some synonyms for categorical: symbolic, nominal, enumerated, discrete

  • There are also two-valued variables known as Boolean or dichotomy

Spectrum of Data Types

  • 1. Nominal = unordered, unmeasurable named categories

  • Example: sunny, overcast, rainy

  • 2. Ordinal = named categories that can be put into a logical order but which have no intrinsic numeric value and no defined distance between them (support < or >)

  • Example: hot, mild, cool

  • 3. Interval = numeric values where the distance between them makes sense (support subtraction) but other operations do not

  • Example: Time expressed in years

  • 4. Ratio = numeric values where all operations make sense

  • These are real or continuous (or possibly integer) values on a scale with a natural 0 point

  • Example: Physical distance

  • In principle, data mining has to handle all possible types of data

  • In practice, applied systems typically have some useful subset of the type distinctions given above

  • You adapt your data to the types provided

2.4 Preparing the Input

  • In practice, preparing the data can take more time and effort than doing the mining

  • Data needs to be in the format required by whatever mining software you’re using

  • In Weka, this is ARFF = attribute relation file format

  • Real data tends to be low in quality

  • Think data integrity and completeness

  • “Cleaning” the data before mining it pays off

  • Weka

  • From Wikipedia, the free encyclopedia

  • Jump to: navigation, search

  • For other uses, see Weka (disambiguation).

  • The Weka or woodhen (Gallirallusaustralis) is a flightless bird species of the railfamily. It is endemic to New Zealand, where four subspecies are recognized. Weka are sturdy brown birds, about the size of a chicken. As omnivores, they feed mainly on invertebrates and fruit. Weka usually lay eggs between August and January; both sexes help to incubate.

  • Behaviour

  • Where the Weka is relatively common, their furtive curiosity leads them to search around houses and camps for food scraps, or anything unfamiliar and transportable.[2]

Gathering the Data Together

  • In a large organization, different departments may manage their own data

  • Global level data mining will require integration of data from multiple databases

  • If you’re lucky, the organization has already created a unified archive, a data warehouse

  • Interesting mining may also require integrating external data into the data set


  • It may be necessary to aggregate data in order to mine it successfully

  • You may have data on parameters of interest spread through multiple instances

  • To be useful to problem solution, it may be necessary to add the values of data points together, for example

  • The type of aggregation is important

  • Remember the aggregation operators in db: COUNT, SUM, AVERAGE, etc.

  • The level of aggregation is important

  • Remember GROUP BY in db

  • Do you aggregate all instances, or is it useful to do it by subsets of some sort?

ARFF (Format)

  • ARFF is the regular version of the data format for Weka

  • XRFF is the XML version

  • In ARFF:

  • % marks a comment

  • @ marks file descriptor information, relation, attributes, and data

  • For a categorical attribute, the set of values is given in braces

  • Categorical values containing spaces have to be put in quotation marks

  • Numeric attributes are simply identified as numeric

  • Instances in the file are given line by line

  • They are separate by new lines

  • Attributes values in instances are separated by commas

  • Missing values (nulls) are indicated with a question mark

  • In an ARFF file, a classification attribute, if there is one, is treated no differently than any others

  • The format is equally suited to classification, association, or cluster mining

  • Figure 2.2, on the following overhead, shows the weather data set in ARFF

Weka Has Three Additional Attribute Types

  • String = the moral equivalent of VARCHAR in db

  • Date = the equivalent of DATE in db

  • Relational = Stay tuned; this will require some explanation

Relational-Valued Attributes

  • The book gives an example which is OK, but it’s not necessarily presented in the clearest way possible

  • My plan is to first give a bunch of explanatory background

  • Then explain the book’s example in a slightly different order than it does

Relational Background

  • Recall that multivalued problems can be viewed as mining the result of a 1-m join

  • In preparing a data set for mining, this is what a relational-valued attribute is:

  • It is an attribute that can contain or consist of multiple instances of the same kind of set of values, where these sets belong together for some reason

  • In a 1-m, pk-fk join, the multiple sets are the rows of the many table which belong together because they share the same fk value

  • In case this general overview isn’t clear, the idea can be illustrated with mothers and children

Mothers and Children

  • Suppose you ran this query:

  • SELECT *

  • FROM Mother, Child

  • WHERE Mother.motherid = Child.motherid

  • GROUP BY motherid

  • Children of the same mother would be grouped together

  • In data mining, it is possible that you would want to elicit information about children in general

  • You might also want to elicit information that generally held for children of the same mother

  • Conceptually, you would be mining information about siblinghood

  • This is where relational-valued attributes come in

  • From a relational point of view, the representation is wrong

  • First normal form says you have flat files with no repeating groups

  • But for data mining purposes, in ARFF format, you want the repeating groups

Explaining the Book’s Example

  • The weather adapts the weather/play a game data to a multivalued example

  • The new twist is this: Games extend over 2 days, not just one

  • Each day is still a single instance

  • But for each game, there are two of these instances which belong together

  • The instances in the rows of the table representing game information will be multivalued

  • Each game will contain two days’ worth of weather data

  • These two days’ worth of data are the contents of one relational-valued attribute in the overall data set

  • Note that in general, relational attributes, multivalued attributes, are not limited to 2 sets of values

  • This is just an artifact of their example, where games last exactly 2 days

  • The book also uses terminology which could be clearer

  • In their new weather table they name the relational attribute “bag”

  • Suffice it to say that it would have been clearer if they had named the attribute game_days or something like that

  • There are three major attributes in the book’s table:

  • bag_ID (id for sets of days belonging to games)

  • bag (multivalued relational attribute containing days belonging to games, grouped by game)

  • play (the classification to play, yes or no)

  • The bag attribute has 4 (familiar) attributes describing the multivalued instances (of day):

  • outlook

  • temperature

  • humidity

  • windy

  • In the body of the ARFF table, the multivalued entries are structured in this way:

  • The data for the multiple days that belong together for a single bag_ID is enclosed in quotation marks

  • Within the quotation marks, the individual sets of day data are separated by “\n”, the new line character

  • The book’s ARFF table is shown in Figure 2.3 on the following overhead

Sparse Data

  • Some data sets are sparse

  • In this context the book means 0’s for numerical values, not nulls

  • Rather than listing everything, a row can be economically expressed by showing only the values present

  • In ARFF, the attributes for a row are:

  • identified by number starting with 0

  • Followed by the value

  • Separated by commas

  • Enclosed in braces

  • E.g.:

  • {1 X, 6 Y, 10 “Class A”}

  • This doesn’t work for nulls; you still have to include ?’s

Attribute Types

  • The bottom line is that ARFF only has two fundamental types: nominal and numeric

  • String attributes are effectively nominal

  • Date attributes are effectively numeric

  • (Recall the discussions of stuff like this in db)

  • The rest of this subsection has to do with numeric types in particular

Numerics as Ordinals

  • The important point is this:

  • Different data mining algorithms treat vanilla numeric values differently

  • One algorithm may treat numerics as ordinals, where subtraction applies, generating rules based on <, =, > comparisons

Numerics as Ratio Values

  • Another algorithm may treat numerics as ratio values

  • Recall that all arithmetic operations are defined in this case

  • The algorithm may normalize ratio values


  • Normalization means putting values into a range, most commonly the range 01

  • A simple approach for positive values: Divide any given data value by the maximum present

  • Another simple approach for positive values: Subtract the minimum from the data value and divide by (max – min)


  • Values can also be statistically standardized

  • Each data point is converted using this approach:

  • xstandardized = (x – μ) / σ

  • This puts the values into a distribution where the mean is 0 and the standard deviation is 1

Distance as an Example of Ratio Values

  • Consider the calculation of distance in n dimensional space, 2-space for example

  • Calculating the square root of the sum of the squares of the differences of the coordinates involves using arithmetic operators other than subtraction

  • Normalization is implicated in a situation like this

  • Given some (x, y) space, suppose x is in the range 010 and y is in the range 0100

  • Do you normalize both x and y before calculating distances or not?

  • Another way of stating this is, do x and y make corresponding contributions to the measure of distance between two data points or not?

Nominal Attributes and Distance

  • This is a crude measure of distance for nominal attributes:

  • If two instances have the same value for that attribute, the distance between them, measure on that attribute is 0

  • If two instances have a different value for an attribute, the distance between them is 1

  • There are cases where nominal attributes can be reverse engineered back to numerics

  • One example from the book: Zip codes and geographic location coordinates

  • Recall that zip codes came up in db in a similar way as determinants of geographic locations

Nominal vs. Numeric

  • Just like in db the assertion is made that an id “number” field should be TEXT—

  • In data mining there may be attributes containing numeric digits which are simply nominal fields and should be mined as such

  • Finally, some algorithms support nominals but not ordinals

  • In the contact lens data, young < pre-presbyopic < presbyopic

  • If their ordinal relationships is not recognized, a complete and correct set of rules can still be mined

  • However a complete and correct set of rules about 1/3 as large can be mined in a system that recognizes the relationship

Missing Values

  • This is essentially a discussion of nulls

  • The only new element consists of two questions:

  • Can you infer anything from the absence of values?

  • Would it be possible to meaningfully code why values are absent and mine something from this?

Inaccurate Values

  • This is essentially a discussion of data integrity

  • Both data miners and regular db users have to cope with faulty data one way or the other

  • The authors say this is especially important when mining

  • It’s especially important to the data miner if the data miner ascribes more significance to an attribute than a regular user does

The End

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