PHENOMENAL DATA MINING: FROM

DATA TO PHENOMENA

John McCarthy

Computer Science Department

Stanford University

Stanford, CA 94305

jmc@cs.stanford.edu

http://www-formal.stanford.edu/jmc/

2001 Oct 20, 11:39 a.m.

Abstract

Phenomenal data mining finds relations between the data and the

phenomena that give rise to data rather than just relations among the

data.

For example, suppose supermarket cash register data does not

identify cash customers. Nevertheless, there really are customers, and

these customers are characterized by sex, age, ethnicity, tastes, income

distribution, and sensitivity to price changes. A data mining program

might be able to identify which baskets of purchases are likely to have

been made by the same customers. In this example, the receipts are

the data, and the customers are phenomena not directly represented

in the data. Once the “baskets” of purchases are grouped by customer,

the way is open to infer further phenomena about the customers, e.g.

their sex, age, etc.

This article concerns what can be inferred by programs about phe-

nomena from data and what facts are relevant to doing this.1 We work

mainly with the supermarket example, but the idea is general.

1In a sense, all data mining is phenomenal; it’s just that the phenomenal part is usually

done by hand. We want the computer to do the phenomenal part also.

INTRODUCTION

In order to infer phenomena from data, facts about their relations

must be supplied. Sometimes these facts can be implicit in the pro-

grams that look for the phenomena, but more generality is achieved

if the facts are represented as sentences of logic in a knowledge base

used by the programs.

The result of phenomenal data-mining might include an extended

database with additional fields on existing relations and new relations.

Thus the relations describing supermarket baskets might be extended

with a customer field, and new relations about customers and their

properties might be introduced.

Introduction

Science and common sense both tell us that the facts about the world are not

directly observable but can be inferred from observations about the effects

of actions. What people infer about the world is not just relations among

observations but relations among entities that are much more stable than

observations. For example, 3-dimensional objects are more stable than the

image on a person’s retina, the information directly obtained from feeling an

object or on an image scanned into a computer. 2 Likewise the fact that a

customer has children is more stable than the fact that a particular basket

includes Roll-ups. The fact that a customer has diabetes is more stable than

a particular pattern of food purchases that may allow inferring that he has

diabetes. The phenomenal facts, partly because they are more stable than

observations, are more predictive of future behavior than simple obsrvational

facts.

The extreme positivist philosophical view that science concerns relations

among observations still influences the design of learning programs, and

that’s what data miners are. However, science never worked that way, nei-

ther do babies and neither should data mining programs. All obtain and use

representations of the objects and use observations only as a means to that

end.

Data mining involves computer programs that infer relations among dif-

ferent kinds of data in large databases. The goal has been to infer useful

2Even very young babies have a lot of innate knowledge of the world. My article

The Well-Designed Child3 concerns what innate knowledge children probably do have

about the world and what knowledge robots should be given. Elizabeth Spelke, [Spe94],

investigates innate knowledge in babies experimentally.

2 PHENOMENA IN THE WORLD

relations that might not have been noticed or at least could not have been

confirmed among this data. We use the standard example of a supermarket

chain with a database of all the cash register receipts for some long time

period—many gigabytes of data. The company wants to mine this database

for information useful for improving its business.

Data-mining can be made to do more than just find relations among data.

Data amounts to observations of the world, and it is possible to infer relations

among entities in the world from the data. Such relations are likely to be as

useful to know about as are relations among the entities directly represented

in the data.

In the supermarket chain example, there are people, groups

of people, their homes with pantries, refrigerators and freezers and facts

about what they cook and what they eat. It should even be possible to infer

the existence of entities in the world, such as previously unidentified groups

of people with distinct eating and purchasing habits. Another example is

to identify bellwether groups; what they buy today, many more will buy

tomorrow.

Moreover, the information will usually admit a more compact description

in terms of the underlying phenomena than in terms of the data.

Although all common sense level knowledge of the world is potentially

relevant to data mining, formalizing common sense has proved to be a dif-

ficult AI problem, and progress has been slow. Nevertheless, we can expect

that certain phenomena will be related to the information in databases in a

straightforward enough way so that information about them can be found

by data miners.

2 Phenomena in the World

What phenomena in the world should a data mining program have built into

it, be told or be able to discover for itself?

At first, knowledge of the general phenomena will be built into the data

miners (data mining programs), and the programs will infer specific values.

Later data miners should use the information expressed in a logical form.

This will permit them to use databases of common sense facts about the

world. Very ambitious data mining projects might hope to make programs

that will come up with entirely new phenomena.

Here are some phenomena and facts relevant to the supermarket domain

together with logical expressions for some of these facts. We give just two

2 PHENOMENA IN THE WORLD

example formulas, and these are not part of a worked out scheme for con-

structing a knowledge base.

people There are the shoppers themselves and also family members. The

data may not identify them directly, but learning about them is the

point of data mining.

Shopper(x) → F amily(x) ⊂ P eople.

(1)

ownership and purchases People buy things and then own them and keep

them somewhere. Maybe the facts about where people keep things are

not relevant for most data mining. The distinction between durable

goods and consumables is important.

Durable(x) ∧ Has(person, x, s) → Has(person, x, Result(U ses(person, x)))¬Durable(x) ∧ Has(person, x, s) → ¬Has(person, x, Result(U ses(person, x)))(2)

possessions Freezers, refrigerators, cars and microwave ovens are items that

some customers will have and others won’t. Having them affects be-

havior.

events The observed events are purchases in the stores for which we have

databases.

Unobserved are the trips to the store and the cooking and eating and

the inspections of the larder. Maybe these can usefully be discrimi-

nated, but maybe they should be lumped into consumption. Other

unobserved events include purchases from the competitors. When a

person purchases a freezer, his status changes to that of a freezer owner

and that fact will persist. The event of acquiring a freezer is more com-

mon than that of giving up the possession of a freezer.

preferences People have preferences among states of affairs—or more specif-

ically among objects.

distributions of properties over people The customers have age, sex,

income and ethnic distributions.

2 PHENOMENA IN THE WORLD

customers appear and disappear There are causes for the appearance

and disappearance of customers, and supermarket chains will be inter-

ested in finding them out. These include moving in or out of the area,

change in family circumstances, advertising campaigns by the chain or

its competitors, changes in the store or its hours of operation, satisfac-

tion or dissatisfaction with goods, prices or service.

The present state of AI is not up to formulating a full common sense

database, but full common sense knowledge is not necessary. We can expect

to do a lot with very limited knowledge. A sophisticated data mining system

might be able to use the following facts in its formulation of hypotheses. An

ambitious logic-based system might use logical expressions of the facts. Less

ambitiously, programmers would use them in designing data mining systems.

1. People persist in time. People want objects. People consume objects and want more. Some objects are permanent on the relevant time scale.
2. Objects are created, appear in stores, sold to customers (people) who use them up and need more.
3. There are kinds of people and kinds of objects.
4. People have attributes, and these attributes change, although some are permanent.
5. People buy objects with money. This uses up money and people do not buy at a rate much higher than they get more money.
6. There is an is-a hierarchy of items and and an is-a hierarchy of people. We suppose these are spelled out in some literature.
7. There is an is-a hierarchy of food.
8. Although it is tempting to organize facts into is-a hierarchies, this is not always possible or appropriate. More complicated predicates and

   functions and logical assertions are sometimes needed to express the

   facts.

9. People are associated into families. Purchases are made for a family. 2 PHENOMENA IN THE WORLD

   10. When food items are purchased, some go into pantries, some into re-

   frigerators, some into freezers and some are eaten right away. When a

   food object is eaten it is removed from where it was stored.

   11. There are bounds on the rate at which people eat. What they don’t

   get from one store they get from another.

   12. A person has an age which increases with time. Very young people are

   children.

   13. There are lots of people an lots of stores. The data miner will have

   information about only some of them.

   14. Customers who buy substantial quantities of frozen or freezable goods

   have freezers.

   15. Owners of microwave ovens can be identified.

   16. Consistent purchase of the most expensive items indicates prosperity.

   It can be asked whether consistent purchase of expensive items is all

   the data miner wants to know anyway. I don’t know about that.

   17. Everybody eats, so food not bought at one store is bought at another.

   18. Suppose a customer comes rarely and always buys frozen spinach in

   bags and a few other items. Inference: the store where he buys most

   of his food doesn’t sell frozen spinach in bags.

   The point is that all the above are a priori facts that may be used to

   infer phenomena. We suppose that only some phenomena need be taken into

   account. For this phenomenal mining we ignore birth and death, physical

   motion, and shape. Mass is taken into account only in connection with

   quantities purchased and rates of consumption.

   It is clear that a very large number of facts are relevant to getting informa-

   tion out of databases of customer purchases. These include general facts of

   common sense and specific facts about consumer properties, consumer goods

   and consumer behavior. I see no alternative to a big project like CyC [LG90]

   for them into a knowledge base by hand. However even a small knowledge

   base may be useful and adequate for experiments.

   3 GROUPING SUPERMARKET PURCHASES BY CUSTOMER

   3 Grouping supermarket purchases by cus-

   tomer

   We propose programs to determine from the cash register receipts which

   baskets were purchased by the same customer. The putative customers can

   then be given identifiers. Programs can infer more facts about customer

   characteristics and behavior with facts about purchases of an identified cus-

   tomer over time than could be inferred from mere statistics about the baskets

   themselves.4

   This example of phenomenal data mining is straightforward in that it is

   reasonably clear what a successful result would be and how it might be used.

   We hope to make it plausible that enough information is present in the data

   to usefully distinguish customers. However, experiment is needed to verify

   that feasible algorithms exist.

   Demographic information about customers is known to be useful, e.g.

   their ages, occupations, sexes and incomes. When this information is sup-

   plied, e.g.

   in mail order situations where credit is granted, it is extensively

   used. However, in our supermarket chain example, that information is not

   in the database of transactions. Let us consider inferring it; it might then be

   used in any of the presently conventional ways.

   There are several approaches to associating baskets purchased by the

   same customer.

   3.1 Minimizing anomaly in assignments of baskets to

   customers

   One approach involves minimizing total anomaly in the assignment of baskets

   to customers.

   Definition: A partial assignment α groups some of the baskets of pur-

   chases according to whether they were purchased by the same customer.

   Each group also includes an identifier c for the customer and a classification

   4It has been suggested that grouping baskets by customer is an example of clustering as

   treated in learning theory. This is incorrect, although there are some similarities. Consider

   two large identical baskets purchased ten minutes apart. Clustering would assign them

   to the same category, but these baskets would almost certainly have been purchased by

   different customers. Identical baskets purchased far enough apart would have an increased

   probability of having been purchased by the same customer, but it wouldn’t be certain.

   Still, the literature on clustering might tell us something useful for the present problem.

   3 GROUPING SUPERMARKET PURCHASES BY CUSTOMER

   class(c) of the customer. The set of baskets associated with the putative

   customer c will be denoted by baskets(c).

   Definition: A complete assignment groups all of the purchase baskets.

   If there are N baskets in the database, there are something like 22N

   complete assignments—less because the customers may be permuted.

   Definition: Associated with each assignment will be a numerical total

   anomaly measuring how anomalous the assignment is. The program’s goal is

   to find an assignment (or maybe many assignments) that minimize the total

   anomaly.

   The total anomaly anom(α) of an assignment α is the sum of two main

   terms,

   anom(α) = anom1(α) + anom2(α).

   anom1(α) is itself a sum

   anom1(α) = (cid:88)

   anom11(c),

   c

   (3)

   (4)

   where the variable c ranges over the set of customers to which the baskets

   are assigned. anom2(α) concerns global properties of the set of assignments.

   Definition: Associated with an assignment α and a customer c is a char-

   acterization char(c, α) of the putative customer. The characterization may

   include qualitative characeristics like sex or owning a freezer, quantitative

   characeristics like age or income group and other customer characteristics

   like a certain purchase signature. The anomaly anom11(c) associated with a

   customer c depends on the characterization char(c, α). Thus buying chew-

   ing tobacco or baby food is more anomalous for some customers than others.

   A program that generates assignments will generate characterizations as it

   groups the baskets by customer. The characterization itself will contribute

   to the anomaly if it is an unusual characterization.

   Definition: A signature is a set of choices among alternate brands or

   sizes of certain commodities. The commodities most useful for signatures

   are those for which variety is not normally considered desirable. While a

   person may want variety in food he is unlikely to want variety per se in dish-

   washing soap, toilet paper or size of dog food. Signatures are included in the

   characterization of a customer.

   The part of the anomaly anom11(c) associated with the putative customer

   c is computed relative to the characterization. Thus if c is characterized as

   3 GROUPING SUPERMARKET PURCHASES BY CUSTOMER

   single young female, a purchase of chewing tobacco should have a higher

   anomaly score than for a male.

   One way of looking at minimizing anomaly of assignments is that we

   want to explain as much of the purchasing behavior as possible by allowable

   characterizations of the customers.

   We regard the notions of minimizing anomaly in the space of assignments

   as a guiding theoretical idea. Programs may find complete assignments, but

   they are unlikely to do it by comparing a large number of alternative complete

   assignments. Instead they are likely to do hill climbing in the space of partial

   assignments.

   Here are some kinds of terms that may be associated with the customer

   part of the anomaly function.

10. A measure of the temporal irregularity of the customer’s purchases. Perishable, non-freezable items like milk need to be purchased at a

    fairly regular rate. If baby food is purchased, it also is consumed at a

    regular rate, although it can be stored. Some customers will be very

    irregular, but an assignment shouldn’t make most of them irregular.

11. A measure of the extent to which the grouped baskets do not fit the characterization char(c, α).

    contribute to the anomaly.

12. Signatures involving a large variation in brands of certain items should
13. A lot of variation in a putative customer’s purchase quantity of a fre- quently bought item. This suggests that the same person didn’t buy

    all those baskets.

14. A customer buys food, stores it for a while and eats it. Thus the contents his larder is a function of time. The database tells about the

    purchasing but not directly about the eating or the state of the larder.

    We can attribute a larder function of time to a customer as part of the

    ascription and use some measure of its irregularity as a component of

    the anomaly.

    Here are some ideas about programs for finding assignments.

15. We hill climb in the space of partial assignments. For example, moving a purchase from one customer to another may reduce the anomaly of

    both customers’ ascribed larder functions.

    3 GROUPING SUPERMARKET PURCHASES BY CUSTOMER

16. We might proceed chronologically, assigning each basket to either a previously postulated customer or to a new one.
17. At first new customers would predominate. However, when the num- ber of postulated customers begins to get too large for the number of

    baskets, the program would try to reduce the number by combining

    baskets.

    How can it be inferred that several cash purchases involved the same

    customer? We only need to be correct often enough so that the statistics

    come out right. Each customer has his own pattern of purchases. Here are

    some considerations.

18. The signature sig(c, α) is a purchase pattern unique to the customer c. Consider items where variety is not normally desired, e.g. dishwasher

    soap. There are several brands, but a customer will normally stick

    with one for quite a long time. If there are 5 brands and 50 such kinds

    of items, there are enough possible signatures to distinguish far more

    customers than a store or even a chain is likely to have. Of course,

    a customer is unlikely to purchase a complete signature package each

    time he goes to the store, so partial signatures will have to be used.

19. The ingredients for particular recipes are sometimes diagnostic, espe- cially when the recipe is unique to the customer or is a standard recipe

    varied in a unique way.

20. An important intermediate variable for a customer is the state of his larder at a given time. He likes to have certain items in stock in his

    refrigerator or freezer.

21. The customer makes choices in a certain pattern, e.g. buys creamy rather than chunky peanut butter. Which choices are made is more

    indicative than whether peanut butter is bought at all on a particular

    occasion, since the customer may not have run out yet.

22. Suppose a store has 10,000 items and has 12,000 customers. Suppose purchases average 20 items. My information theory intuition suggests

    that there is enough information to identify the customers over some

    20 shopping trips. The information theory numbers can be analyzed,

    but experiment is still required to determine feasibility.

    4 THE CUSTOMER AS A STOCHASTIC PROCESS

23. Sometimes it will be impossible to assign a basket to a customer. As an extreme example, suppose that withing ten minutes two customers

    each buy a six pack of the same brand of beer and nothing else. Which

    one made which purchase will be impossible to tell, but it won’t matter

    which purchase is assigned to which customer.

    4 The Customer as a Stochastic Process

    The methods discussed in section 3 group purchases by customer. However,

    the specific purchases made by the customer are of interest only in so far as

    they enable prediction of his future behavior and how he might respond to

    things the store might do, e.g. advertisements, sales, changes in products

    offered, changes in prices.

    In general, we might regard the customer as a stochastic process, i.e.

    what he will buy (and whether he will come to the store at all), depends

    probabilistically on the state of his larder, and the actions of the store.

    A regular customer may visit the store once per week for 5 years, i.e.

    make 250 visits. Some may make as many as 1,000 visits. Nevertheless,

    there often won’t be enough information to make a very sophisticated model

    of a customer. Therefore, simplified models are worth considering.

    The simplest model is that customer c has probability p(c, i) of buying

    item i. The matrix ||p(c, i)|| is likely to be approximable by a matrix of much

    lower rank, i.e. the customers form a space of lower dimension. If this is true,

    customers can be approximately characterized by a much smaller number of

    parameters than are needed for a complete probability distribution. This in

    turn means that accurate information about the customers can be obtained

    with smaller samples that would otherwise be required. If the assumption of

    independence of the members of the signature is valid, it still takes quite a

    lot of information to characterize the customer.

    The next more elaborate model might take into account the state of the

    customer’s larder. He won’t buy more of certain items until he has consumed

    what he previously bought. If we regard the customer’s state as given by the

    contents of his larder, we can regard his purchases as determined by a Markov

    process.

    The model might be further elaborated to take into account his probable

    response to sales, etc. Economists would be tempted to try to ascribe a

    demand curve, most likely just two numbers—the demand at a base price

    5 MAIL ORDER BOOKSTORES

    and an elasticity.

    We will not pursue these elaborations further in this article, but it seems

    likely that the most useful information to supermarket companies doing data

    mining will involve the probabilities of response of different kinds of cus-

    tomers to different stimuli.

    5 Mail Order Bookstores

    Consider a store selling books by mail. The customers are identified, so we

    don’t have that problem.

    However, we can suppose that many characteristics of customers are not

    identified such as age, income, occupation. Literary tastes can be identified

    in so far as they correspond to a tendency to buy books in predefined clas-

    sifications. However, the data may allow inferring new classifications with

    respect to which the customers behave more consistently than with respect

    to the traditional classifications.

    Some classes of customers are leaders in that their preferences today can

    be used to predict the market for a book later. Identifying such customers

    and classes of customers may be useful.

    The above phenomena—age, etc.—are not in the data per se, but they are

    rather close to it. Their identification should not be as ambitious a project as

    identifying the customers of a supermarket. Almost all of the computations

    will involve the individual customers. The leadership phenomenon involves

    more but still has a rather simple character.

    6 Proposed Experiments

    Grouping supermarket purchases by customer as proposed in Section 3 can

    be tested with the aid of a supermarket database that does contain customer

    identification. We discard the customer identification, run our grouping al-

    gorithm and compare the results with the genuine customer data.

    My present opinion is that grouping baskets by customers is likely to be

    a difficult but feasible task. As will be seen, it will involve taking advantage

    special features of the behavior of supermarket customers. In this respect, it

    may resemble cryptanalysis which often takes advantage of special features

    of the behavior of senders of messages. Moreover, the results cannot be per-

    6 PROPOSED EXPERIMENTS

    fect in terms of identifying the purchasers, but the uncertainties about who

    bought what may not affect the interesting statistics of customer behavior.

    Here are some ideas about how to proceed.

24. It may be best to start the experiments with a relatively small store. That way there will be fewer assignments to try and fewer similar

    signatures.

25. Very likely we should start with a date in the middle of the operation of the system and try to extend identifications both forward and backward

    in time.

26. At any time in the computation, there will be a certain collection of putative customers and a set of possible assignments of some of the

    baskets to customers. Maybe the computational resources will be ad-

    equate to deal with hundreds to thousands of possible assignments.

    Each of these assignments will have an anomaly computed on the basis

    of what has been assigned so far.

27. Since many people shop on a weekly basis, it may be worthwhile to try to find some putative customers who buy on a particular day of the

    week.

28. It may be possible to find some signatures for some customers that are repeated every week. For example, a shopper may buy both whole

    milk and skim milk every time, because of the needs of different family

    members.

29. The algorithm may grow assignments forward and backward in time. As it goes it will eliminate certain assignments.
30. When it cannot decide among the assignments over some lengthy pe- riod, say two months, it should probably just pick one in order to keep

    down the number of open choices.

31. Perhaps there will be a compact way of keeping certain choices open in order to use long term aspects of the signature.

    7 THE LOGIC OF PHENOMENAL DATA MINING

    7 The Logic of Phenomenal Data Mining

    The ways in which mathematical logic has been used in database

    theory and database systems are likely to require extension for

    phenomenal data mining..

    Database theory and database system commonly use mathematical logic

    to represent facts. However, subsets of logic are adequate for most present

    database systems. For example, the databases can often be considered as

    collections of ground literals, i.e. predicate symbols applied to constants.

    More general sentences are used as rules and given a special status.

    One example that immediately arises in the supermarket problem is the

    fact that the customers who bought particular baskets are unknown, and it

    is not known a priori whether two given baskets were bought by the same

    customer. In Prolog and similar systems, the unique names hypothesis, i.e.

    that distinct constant expressions represent distinct objects, is usually built

    into the system.

    Consider

    buyer(b1) = buyer (b2),

    which asserts that baskets b1 and b2 were purchased by the same customer.

    Unlike the common situation in database systems, the truth of this assertion

    is not in the database. Neither is a unique identifier for b1 available.

    The set of customers is unknown, although it is known to exist. Facts

    about it may be known or conjectured.

    7.1 Ontology

    We follow Quine in taking the ontology of a system to be given by the col-

    lection of domains over which variables range. In the supermarket example,

    we may have

    products These can be represented by their product codes.

    purchased items The particular instances of items purchased as part of a

    particular basket.

    baskets The collection of items purchased on a particular occasion.

    customers The set of customers is unknown but is known to exist .

    8 REMARKS.

    8 Remarks.

32. Suppose a customer of type i has a probability Pij of including item j in a basket. We can infer an approximate number of types by looking

    at the approximate rank of the matrix Pij.

33. Classifying customers into discrete types may not give as good results as a more complex model that take into account the age of the customer

    as a continuous variable.

34. A linear relation between phenomena and observations is the simplest case, and such relations can probably discovered by methods akin to

    factor analysis.

35. We could infer that there were two subpopulations if we didn’t already know about sex.
36. We might infer from data from our stores in India, that there was a substantial part of the population that didn’t purchase meat products.

    We can tell this from a situation in which everyone buys meat but less,

    because certain other purchase patterns are associated with not buying

    meat.

37. Tire mounting services are purchased in connection with the purchase of tires. The phenomenon is that tires are useless unless mounted. Does

    knowing this fact give more than just the correlation?

38. Suppose a new item, e.g. a hula hoop, is increasing its sales rapidly, and 5 percent of the customers have bought it. Suppose, however, that the

    customers that buy it rarely buy another, and these customers are only

    those with young girls in the family, and those customers have almost

    all bought one. Under these hypotheses, which identifying customers

    might verify, it is reasonable to conclude that the fad for hula hoops

    has reached its peak, and that if a lot more are ordered, the store is

    likely to be stuck with them.

39. Suppose we have the baskets grouped by customer—either because the data was given or because we have inferred it as described above. Can

    we determine how far the customers live from the store? The informa-

    tion might be useful in anticipating how much business might be lost to

    9 ACKNOWLEDGMENTS

    a newly opened competitor. No immediate idea occurred to me when I

    thought of the question. However, it is rash to conclude that it can’t be

    done. Someone cleverer than I, or who knows more about customers of

    supermarkets, might figure a way. One just shouldn’t jump to negative

    conclusions.

40. Grouping by customer might permit observing that no-one who buys item 531 ever buys anything from that store again. Such a fact would

    not show up as a direct correlation in the data unless item 531 were

    bought in quantities that significantly affected sales of some other

    items.

    10. If a customer buys a certain product but doesn’t buy a necessary com-

    plementary product, we can infer that he buys the complementary

    product from someone else.

    The only experimental work with phenomenal data mining is reported in

    [LT98].

    9 Acknowledgments

    I am indebted to Rakesh Agrawal of the IBM Almaden Research Laboratory

    for introducing me to data mining in conversation and through his papers. I

    also had useful discussions with Ted Selker, Joe Halpern, Jeff Ullman, Nimrod

    Megiddo, Rajeev Motwani, Brad Efron, Christos Papadimitriou, Ron Fagin,

    Tom Costello, and Gregory Tseytin.

    This work was partly supported by ARPA (ONR) grant N00014-94-1-

    0775 and partly by IBM while I was visiting faculty during the summer of

    1996 and since.

    References

    [LG90] Douglas B. Lenat and R. V. Guha. Building Large Knowledge-

    Based Systems: Representation and Inference in the CYC Project.

    Addison-Wesley, 1990.

    REFERENCES

    [LT98] Donal Lyons and Gregory S. Tseytin. Phenomenal data mining and

    link analysis. In David Jensen and Cochairs Henry Goldberg, edi-

    tors, Artificial Intelligence and Link Analysis, 1998 Fall Symposium,

    pages 68–75. AAAI, AAAI Press, 1998.

    [Spe94] Elizabeth Spelke.

    50:431–445, 1994.

    Initial knowlege:

    six suggestions. Cognition,

    /@steam.stanford.edu:/u/jmc/e96/data-mining.tex: begun 1996 Jul 5, latexed 2001 Oct 20 at 11:39 a.m.