VSSML16 L3. Clusters and Anomaly Detection

September 8-9, 2016

BigML, Inc 2

Cluster Analysis

Poul Petersen CIO, BigML, Inc

Finding Similarities

BigML, Inc 3Unsupervised Learning

Trees vs Clusters

Trees (Supervised Learning)

Provide: labeled data Learning Task: be able to predict label

Clusters (Unsupervised Learning)

Provide: unlabeled data Learning Task: group data by similarity


Trees vs Clusters

sepallength

sepalwidth

petallength

petalwidth species

5.1 3.5 1.4 0.2 setosa5.7 2.6 3.5 1.0 versicolor6.7 2.5 5.8 1.8 virginica… … … … …

sepallength

sepalwidth

petallength

petalwidth

5.1 3.5 1.4 0.25.7 2.6 3.5 1.06.7 2.5 5.8 1.8… … … …

Inputs “X” Label “Y”

Learning Task: Find function “f” such that: f(X)≈Y

Learning Task: Find “k” clusters such that the data in each cluster is self similar


Use Cases

• Customer segmentation • Item discovery • Similarity • Recommender • Active learning


Customer Segmentation

GOAL: Cluster the users by usage statistics. Identify clusters with a

higher percentage of high LTV users. Since they have similar usage

patterns, the remaining users in these clusters may be good

candidates for up-sell.

• Dataset of mobile game users.

• Data for each user consists of usage statistics and a LTV based on in-game purchases

• Assumption: Usage correlates to LTV

0%

3% 1%


Item Discovery

GOAL: Cluster the whiskies by flavor profile to discover whiskies that have

similar taste.

• Dataset of 86 whiskies

• Each whiskey scored on a scale from 0 to 4 for each of 12 possible flavor characteristics.

Smoky

Fruity


Clustering Demo #1


Similarity

GOAL: Cluster the loans by application profile to rank loan

quality by percentage of trouble loans in population

• Dataset of Lending Club Loans

• Mark any loan that is currently or has even been late as “trouble”

0%

3%

7%

1%


Active Learning

GOAL: Rather than sample randomly, use clustering to group

patients by similarity and then test a sample from each cluster to label the data.

• Dataset of diagnostic measurements of 768 patients.

• Want to test each patient for diabetes and label the dataset to build a model but the test is expensive*.


*For a more realistic example of high cost, imagine a dataset with a billion transactions, each one needing to be labelled as fraud/not-fraud. Or a million images which need to be labeled as cat/not-cat.

2323

Active Learning


Human ExampleCluster into 3 groups…


Human Example


Learning from Humans• Jesa used prior knowledge to select possible

features that separated the objects. • “round”, “skinny”, “edges”, “hard”, etc • Items were then clustered based on the chosen

features • Separation quality was then tested to ensure: • met criteria of K=3 • groups were sufficiently “distant” • no crossover


Learning from Humans

• Length/Width

• greater than 1 => “skinny”

• equal to 1 => “round”

• less than 1 => invert

• Number of Surfaces

• distinct surfaces require “edges” which have corners

• easier to count

Create features that capture these object differences


Cluster FeaturesObject Length / Width Num Surfaces

penny 1 3

dime 1 3

knob 1 4

eraser 2.75 6

box 1 6

block 1.6 6

screw 8 3

battery 5 3

key 4.25 3

bead 1 2


Plot by Features

K=3

NumSurfaces

Length / Width

box block eraser

knob

pennydime

bead

key battery screw

K-Means Key Insight:We can find clusters using distances

in n-dimensional feature space


Plot by Features

NumSurfaces

Length / Width

box block eraser

knob

pennydime

bead

key battery screw

K-MeansFind “best” (minimum distance)circles that include all points


K-Means Algorithm

K=3


K-Means Algorithm

K=3


Features Matter

Metal Other

Wood


Convergence

Convergence guaranteedbut not necessarily unique

Starting points important (K++)


Starting Points• Random points or instances in n-dimensional space

• Chose points “farthest” away from each other

• but this is sensitive to outliers

• k++

• the first center is chosen randomly from instances

• each subsequent center is chosen from the remaining instances with probability proportional to its squared distance from the point's closest existing cluster center


Scaling

price

number of bedrooms

d = 160,000

d = 1


Other Tricks

• What is the distance to a “missing value”?

• What is the distance between categorical values?

• What is the distance between text features?

• Does it have to be Euclidean distance?

• Unknown “K”?


Distance to Missing Value?• Nonsense! Try replacing missing values with:

• Maximum

• Mean

• Median

• Minimum

• Zero

• Ignore instances with missing values


Distance to Categorical?

• Special distance function

• if valA == valB then distance = 0 (or scaling value) else distance = 1

• Assign centroid the most common category of the member instances

Approach: similar to “k-prototypes”


Distance to Categorical?

feature_1 feature_2 feature_3

instance_1 red cat ball

instance_2 red cat ball

instance_3 red cat box

instance_4 blue dog fridge

D = 0

D = 1

D = sqrt(3)

Compute Euclidean distance between discrete vectors


Text Vectors

1Cosine Similarity

0

"hippo" "safari" "zebra" ….

1 0 0 …

1 1 0 …

0 0 0 …

Text Field #1

Text Field #2

Cosine Distance = 1 - Cosine Similarity

Features(thousands)


Finding K: G-Means


Finding K: G-Means


Finding K: G-MeansLet K=2

Keep 1, Split 1 New K=3


Finding K: G-MeansLet K=3Keep 1, Split 2New K=5


Finding K: G-MeansLet K=5K=5


Clustering Demo #2

BigML, Inc 2

Anomaly Detection

Poul Petersen CIO, BigML, Inc

Finding the Unusual


Clusters vs Anomalies

Clusters (Unsupervised Learning)

Provide: unlabeled dataLearning Task: group data by similarity

Anomalies (Unsupervised Learning)

Provide: unlabeled data Learning Task: Rank data by dissimilarity


Clusters vs Anomalies

sepal length

sepal width

petal length

petal width

5.1 3.5 1.4 0.25.7 2.6 3.5 1.06.7 2.5 5.8 1.8… … … …

Learning Task: Find “k” clusters such that the data in each cluster is self similar

sepal length

sepal width

petal length

petal width

5.1 3.5 1.4 0.25.7 2.6 3.5 1.06.7 2.5 5.8 1.8… … … …

Learning Task: Assign value from 0 (similar) to 1 (dissimilar) to each instance.


• Unusual instance discovery

• Intrusion Detection

• Fraud

• Identify Incorrect Data

• Remove Outliers

• Model Competence / Input Data Drift

Use Cases


Removing Outliers

• Models need to generalize • Outliers negatively impact generalization

GOAL: Use anomaly detector to identify most anomalous points and then remove them before modeling.

DATASET FILTERED DATASET

ANOMALY DETECTOR

CLEAN MODEL


Anomaly Demo #1


Intrusion Detection

GOAL: Identify unusual command line behavior per user and across all users that might indicate an intrusion.

• Dataset of command line history for users • Data for each user consists of commands,

flags, working directories, etc. • Assumption: Users typically issue the same

flag patterns and work in certain directories

Per User Per Dir All User All Dir


Fraud

• Dataset of credit card transactions • Additional user profile information

GOAL: Cluster users by profile and use multiple anomaly scores to detect transactions that are anomalous on multiple levels.

Card Level User Level Similar User Level


Model Competence• After putting a model it into production,

data that is being predicted can become statistically different than the training data.

• Train an anomaly detector at the same time as the model.

G O A L : F o r e v e r y prediction, compute an anomaly score. If the anomaly score is high, then the model may not be competent and should not be

Training Data

PREDICTION

ANOMALY SCORE

MODEL

ANOMALY DETECTOR


Univariate Approach• Single variable: heights, test scores, etc

• Assume the value is distributed “normally”

• Compute standard deviation

• a measure of how “spread out” the numbers are

• the square root of the variance (The average of the squared differences from the Mean.)

• Depending on the number of instances, choose a “multiple” of standard deviations to indicate an anomaly. A multiple of 3 for 1000 instances removes ~ 3 outliers.


Univariate Approach

measurement

frequency

outliersoutliers

• Available in BigML API


Benford’s Law• In real-life numeric sets the small digits occur disproportionately often as

leading significant digits.

• Applications include:

• accounting records

• electricity bills

• street addresses

• stock prices

• population numbers

• death rates

• lengths of rivers

• Available in BigML API


Human ExampleMost Unusual?


Human Example“Round”“Skinny” “Corners”

“Skinny” but not “smooth”

No “Corners”

Not “Round”

Key InsightThe “most unusual” object

is different in some way fromevery partition of the features.Most unusual


Human Example• Human used prior knowledge to select possible

features that separated the objects.

• “round”, “skinny”, “smooth”, “corners”

• Items were then separated based on the chosen features

• Each cluster was then examined to see which object fit the least well in its cluster and did not fit any other cluster


Learning from Humans

• Length/Width

• greater than 1 => “skinny”

• equal to 1 => “round”

• less than 1 => invert

• Number of Surfaces

• distinct surfaces require “edges” which have corners

• easier to count

• Smooth - true or false

Create features that capture these object differences


Anomaly FeaturesObject Length / Width Num Surfaces Smooth

penny 1 3 TRUE

dime 1 3 TRUE

knob 1 4 TRUE

eraser 2.75 6 TRUE

box 1 6 TRUE

block 1.6 6 TRUE

screw 8 3 FALSE

battery 5 3 TRUE

key 4.25 3 FALSE

bead 1 2 TRUE


smooth = True

length/width > 5

box

blockeraser

knob

pennydime

bead

key

battery

screw

num surfaces = 6

length/width =1

length/width < 2

Random SplitsKnow that “splits” matter - don’t know the order


Isolation Forest

Grow a random decision tree until

each instance is in its own leaf

“easy” to isolate

“hard” to isolate

Depth

Now repeat the process several times and

use average Depth to compute anomaly

score: 0 (similar) -> 1 (dissimilar)


Isolation Forest Scoring

f_1 f_2 f_3i_1 red cat balli_2 red cat balli_3 red cat boxi_4 blue dog pen

D = 3

D = 6

D = 2

Score


• A low anomaly score means the loan is similar to the modeled loans.

• A high anomaly score means you can not trust the model.

Model Competence

Prediction T T

Confidence 86% 84%

AnomalyScore 0.5367 0.7124

Competent? Y N

OPEN LOANS

PREDICTION

ANOMALY SCORE

CLOSED LOAN MODEL

CLOSED LOAN ANOMALY DETECTOR


Anomaly Demo #2

Date post:	07-Feb-2017
Category:	Data & Analytics
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VSSML16 L3. Clusters and Anomaly Detection

Data & Analytics