Data Clustering for Forecasting - MIT...

1

Data Clustering for ForecastingJames B. Orlin

MIT Sloan School and OR Center

Mahesh KumarMIT OR Center

Nitin PatelVisiting Professor

Jonathan WooProfitLogic Inc.

2

Overview of Talk

• Overview of Clustering

• Error-based clustering

• Use of clustering in forecasting

• But first, a few words from Scott Adams

6

What is clustering

Clustering is the process of partitioning a set of data or objects into clusters with the following properties:

• Homogeneity within clusters: data that belong to the same cluster should be as similar as possible

• Heterogeneity between clusters: data that belong to different clusters should be as different as possible.

7

Overview of this talk

• Provide a somewhat personal view of the significance of clustering in life, and why it has not met its promise

• Provide our technique for how to incorporate uncertainty about data into clustering, so as to reduce uncertainty in forecasting.

8

Iris Data (Fisher, 1936)

Species Setosa Versicolor Virginica

can2

can1

9

Cluster the iris data

• This is a 2-dimensional projection of 4-dimensional data. (sepal length and width, petal length and width)

• It is not clear if there are 2, 3 or 4 clusters

• There are 3 clusters

• Clusters are usually chosen to minimize some metric (e.g., sum of squared distances from center of the cluster)

10

Iris Data


can2

can1

11

Iris Data, using ellipses


can2

can1

12

Why is clustering important: a personal perspective

• Two very natural aspects of intelligence:– grouping (clustering) and categorizing– It’s an organizing principle of our minds and of our life

• Just a few examples– We cluster life into “work life” and “family life”– We cluster our life by our “roles” father, mother, sister,

brother, teacher, manager, researcher, analyst, etc– We cluster our work life into various ways, perhaps organized

by projects, or who we report to, or by who reports to us, etc.– We even cluster what talks we attend, perhaps organized by

quality, or what we learned, or where it was.

13

More on Clustering in Life• More clustering Examples:

– Go shopping: products are clustered in the store (useful for locating things)

– As a professor: I need to cluster students into letter grades: “what really is the difference between a B+

and an A- ? (useful in evaluations)– When we figure out what to do, we often prioritize by

clustering things (important vs. non-important)– We cluster people into multiple dimensions based on

appearance, intelligence, character, religion, sexual orientation, place of origin, etc

• Conclusion: Humans are clustering and categorizing by nature. It is part of our nature. It is part of our intelligence

14

Fields that have used clustering

• Marketing (market segmentation, catalogues)• Chemistry (the periodic table is a great example)• Finance (making sense of stock transactions)• Medicine (clustering patients) • Data mining (what can we do with transactional data,

such as click stream data?)• Bioinformatics (how can we make sense of proteins?)• Data compression and aggregation (can we cluster

massive data sets into smaller data sets for subsequent analysis?

• plus much more

15

Has clustering been successful in data mining?

• Initial hope: clustering would find many interesting patterns and surprising relationships– arguably not met, at least not nearly enough– perhaps it requires too much intelligence– perhaps we can do better in the future

• Nevertheless: clustering has been successful in use computers for things that humans are quite bad at– dealing with massive amounts of data– effectively using knowledge of “uncertainty”

16

An issue in clustering: the effect of scale

• Background: an initial motivation for our work in clustering (as sponsored by the e-business Center) is to eliminate the effect of scale in clustering

17

A Chart of 6 Points

Clustering 6 points

0123456

1.5 2 2.5 3 3.5 4 4.5

18

Two Clusters of the 6 Points

Clustering 6 points

0123456

1.5 2 2.5 3 3.5 4 4.5

19

We added two points and adjusted the scale

Clustering 8 points

3.5

4

4.5

5

5.5

0 10 20 30 40 50 60

20

3 clusters of the 8 pointsClustering 8 points

3.5

4

4.5

5

5.5

0 10 20 30 40 50 60

The 6 points on the left are clustered differently

21

Scale Invariance

• A clustering approach is called “scale invariant” if it develops the same solution, independent of the scales used

• The approach developed next is scale invariant

22

Using clustering to reduce uncertainty. Try to find the average of the 3 populations


can2

can1

23

0.48 0.5 0.52 0.54 0.56 0.58 0.6

Using uncertainty to improve clustering: an example with 4 points in 1 dimension

The four points were obtained as sample means for four samples, two from one distribution, and two from another.

Objective: cluster into two groups of two each so as to maximize the probability that each cluster represents two samples from the same distribution.

24

Standard Approach

Consider the four data points, and cluster based on these values.

Resulting cluster

0.48 0.5 0.52 0.54 0.56 0.58 0.6

25

Incorporating Uncertainty• a common assumption in statistics

– data comes from “populations” or distributions– from data, we can estimate the mean of the population and the

standard deviation of the original • Usual approach to clustering

– keep track of the estimated mean– ignore the standard deviation (estimate of the error)

• Our approach: use both the estimated mean and the estimate of the error.

0.48 0.5 0.52 0.54 0.56 0.58 0.6

26

0.48 0.5 0.52 0.54 0.56 0.58 0.6

The two samples on the left were samples with 10,000 points each. The samples on the right were two samples with 100 points each.

The radius corresponds to standard deviation.

Smaller circles !!!! larger data sets !!!! more certainty.

27

0.48 0.5 0.52 0.54 0.56 0.58 0.6

probability = 4/19

probability = 8/19

probability = 7/19

28

0.48 0.5 0.52 0.54 0.56 0.58 0.6

0.48 0.5 0.52 0.54 0.56 0.58 0.6

10,000 points with mean .501

100 points with mean .562



100 points with mean .592


True mean: .5

True mean: .53

29

More on using uncertainty

• We will use clustering to reduce uncertainty

• We will use our knowledge of the uncertainty to improve the clustering

• In the previous example, the correct cluster was

• We had generated 20 sets of four points at random. The data was from the second set of four points.

probability = 8/19

30

Error based clustering

1. Start with n points in k-dimensional space – next example has 15 points, 2 dimensions– Each point has an estimated mean as well as a

standard deviation of the estimate2. Determine the likelihood for each pair of

points coming from the same distribution3. Merge the two points with the greatest

likelihood4. Return to Step 2.

31

Using Maximum Likelihood

• Maximum Likelihood Method– Suppose we have G clusters, C1, C2, …, CG. Out of

exponentially many clusterings possible, which clustering is most likely w.r.t. to the observed data.

Objective:

Computationally difficult!

12 2 2

1

1max ( ) ( ) ( )k k k

Gti i

k i C i C i Ci i i

x xσ σ σ

−

= ∈ ∈ ∈∑ ∑ ∑ ∑

32

Heuristic solution based on maximum likelihood

• Greedy heuristic– Start with n single point clusters– Combine pair of clusters that lead to maximum increase in the

objective value (based on maximum likelihood)– Stop when we have G clusters.

Similar to hierarchical Clustering

33

Error-based clustering

• At each step combine pair of clusters Ci, Cj with smallest – xi , xi : maximum likelihood of means of clusters– """"i , """"j : standard errors in x’s.

• We define the distance between two clusters as

Computationally much easier!!

2 2 1( ) ( ) ( )ti j i j i jx x x xσ σ −− + −

2 2 1( ) ( ) ( )ti j i j i jx x x xσ σ −− + −

34

Error-based Clustering Algorithm

• distance(Ci, Cj) =

• Start with n singleton clusters• At each step combine pair of clusters Ci, Cj with

smallest distance.• Stop when we have desired number of clusters

It is a generalization of Ward’s method.

2 2 1( ) ( ) ( )ti j i j i jx x x xσ σ −− + −

35

The mean is the dot. The error is given by the ellipse.

A small ellipse means that the data is quite accurate.

36

Determine the two elements most likely to come from the same distribution.

Merge them into a single element.

37

Determine the two elements most likely to come from the same distribution.

Merge them into a single element.

38

Continue this process, reducing the number of clusters one at a time.

50

Here we went all the way to a single cluster.

We could stop with 2 or 3 or more clusters. We can also evaluate different numbers of clusters at the end.

51

Rest of the Lecture

• The use of clustering in forecasting developed while Mahesh Kumar worked at ProfitLogic.

• Joint work: Mahesh Kumar, Nitin Patel, Jonathon Woo.

52

Motivation

• Accurate sales forecasting is very important in retail industry in order to make good decisions.

Manufacturer Wholesaler Retailer Customer

Shipping Allocation Pricing

Kumar et al. used clustering to help in accurate sales forecasting.

53

Forecasting Problem

• Goal: Forecast Sales• Parameters that affect sales

– Price– When a product is introduced– Promotions– Inventory– Base demand as a function of time of the year.– Random effects.

54

Seasonality Definition

• Seasonality is the hypothesized underlying base demand of a group of similar merchandize as a function of time of the year.

• It is a vector of size 52, describing variations over the year.

• It is independent of external factors like changes in price, promotions, inventory, etc. and is modeled as a multiplicative factor.

• e.g., two portable CD players have essentially the same seasonality, but they may differ in price, promotions, inventory, etc.

55

Seasonality Examples (made up data)

weekly sales for summer shoes

weekly sales for winter boots

56

Objective: determine seasonality of products

• Difficulty: observations of a product’s seasonality is complicated by so other factors– when the product is introduced– sales and promotions– inventory

• Solution methods– preprocess data to compensate for sales and promotions and

inventory effects– average over lots of very similar products to eliminate some of

the uncertainty– Further clustering of products can eliminate more uncertainty

57

Retail Merchandize Hierarchy

Shoes

Men’ssummerShoes

Sales data available for items

Chain

Department

Class

Item

J-Mart

Debok walkers

58

Modeling Seasonality

• Seasonality is modeled as a vector with 52 components

• Assumptions:– We assume errors are Guassian– We treat the estimate of the σσσσ’s as if they are the correct values

2 2 2 21 1 2 2 52 52{( , ), ( , ),..., ( , )} ( , )i i i i i i i i iSeas x x x xσ σ σ σ= =

59

Illustration on simulated data

• Kumar et al generated data with 3 different seasonalities.

• They then combined similar products and produced estimates of seasonalities.

• Clustering produced much better final estimates.

60

Simulation Study

• 3 different seasonalities were used to generate sales data for 300 items.

• All 300 items divided into 12 classes.

• 12 estimates of seasonality coefficients along with associated errors.

• Used clustering into three clusters to forecast correct seasonalities.

61

Seasonalities

62

Initial seasonality estimates

63

Clustering

• Cluster classes with similar seasonality to reduce errors. – Example: Men’s winter shoes, men’s winter coats.

• Standard Clustering methods do not incorporate information contained in the errors.– Hierarchical clustering– K-means clustering– Ward’s method

64

Further Clustering

• They used K-means, hierarchical, and Ward’s technique

• They also used error based clustering

65

Kmeans, hierarchical (avg), Ward’s Result

66

Error-based Clustering Result

67

Real Data Study

• Data from retail industry.• 6 department: books, sporting goods, greeting cards,

videos, etc.• 45 classes.• Sales forecast

– Without clustering– Standard clustering– Error-based clustering

68

Forecast Result (An example)

Weeks

Sale

s

No Clustering

Standard Clustering

Error-based Clustering

69

Result Statistics

• Average Forecast Error

ForecastSale ActualSaleActualSale

−=∑

∑

70

Summary and Conclusion

• A new clustering method that incorporates information contained in errors

• It has strong theoretical justification under appropriate assumptions

• Computationally easy

• Works well in practice

71

‘Summary and Conclusion

• Major point: if one is using clustering to reduce uncertainty, then it makes sense to use error-based clustering.

• Scale invariance.

• Error-based clustering has strong theoretical justification and works well in practice.

• The concept of using errors can be applied to many other applications where one has reasonable estimate of errors.

Data Clustering for Forecasting - MIT...

Documents

Transcript of Data Clustering for Forecasting - MIT...