Week 5: Data Preparation Flashcards by Annie Clarnette

REVERSED

from py_stringmatching import similarity_measure as sm

What is the python library for computing similarity measures?

How well did you know this?

Not at all

Perfectly

REVERSED

Some hierarchies can be automatically generated based on the analysis of the number of distinct values per attribute in the dataset
The attribute with the most distinct values is placed at the lowest level of the hierarchy
E.g. Country (highest level) -> state -> city -> street (lowest level)
This is also a type of data smoothing

What is concept hierarchy generation?

How well did you know this?

Not at all

Perfectly

REVERSED

Effective if data is clustered but not if data is “smeared”

When is data reduction through clustering useful and when is it not useful?

How well did you know this?

Not at all

Perfectly

REVERSED

Random error or variance in a measured variable

What is noise in data?

How well did you know this?

Not at all

Perfectly

REVERSED

Stepwise forward selection: starts with empty set of attributes. Best of original attributes are determined and added to the set at each step
Stepwise backward elimination: starts with full set of attributes. At each step, removes worst of remaining attributes
Combination of forward selection and backward elimination: start with empty set, combine methods so that at each step the procedure adds the best attribute to reduced set and removes the worst attribute from initial set
Decision tree induction: tree is constructed from given data. All attributes that do not appear in the tree are considered irrelevant

What are the 4 heuristic methods for selecting the subset in attribute subset selection?

How well did you know this?

Not at all

Perfectly

REVERSED

Quantifies the local density of a data point with the use of a neighbourhood of size k
-Introduces a smoothing parameter: reachability distance RD
RDk(x,y) = max{K dist(x), dist(x,y)}, where K dist(x) is the distance between x and its K-nearest neighbour
-the local reachability distance of point x is:
LRDk(x) = k/[sum of y in KNN(x) * RDk(x,y)]
-the local outlier factor LOF is:
LOFk(x) = sum of y in [KNN(x)*LRDk(y)/LRDk(x)] / k

-Generally, LOF >1 means x has a lower density than its neighbours

What is the local outlier factor for outlier detection?

How well did you know this?

Not at all

Perfectly

REVERSED

lev_sim = sm.levenshtein.Levenshtein()
lev_sim.get_sim_score (s1, s2)

How do you compute the levenshtein similarity between strings s1 and s2 in python?

How well did you know this?

Not at all

Perfectly

REVERSED

Conformance to schema: evaluate constraints on a snapshot
Conformance to business rules: evaluate constraints on changes in the database
Accuracy: perform inventory (expensive), or use proxy (track complaints)
Glitches in analysis
Successful completion of end-to-end process

What are examples of data quality metrics? (5)

How well did you know this?

Not at all

Perfectly

REVERSED

Novelty detection involves seeing if new data fits with an existing data or would be considered an outlier

What is the difference between outlier detection and novelty detection?

How well did you know this?

Not at all

Perfectly

REVERSED

Attributes that duplicate much or all of the information contained in one or more other attributes

What are redundant attributes?

How well did you know this?

Not at all

Perfectly

REVERSED

Transform the multi aria text outlier detection task into a univariate outlier detection problem

What is the general approach for outlier detection with multivariate data?

How well did you know this?

Not at all

Perfectly

REVERSED

Supervised: use class information
Bottom-up merge: find the best neighbouring intervals to merge
Initially each distinct value is an interval, Chi squared tests are performed on every adjacent interval and those with the least chi squared values are merged together. Merge performed recursively until a predefined stopping condition is satisfied

What is correlation analysis for discretisation?

How well did you know this?

Not at all

Perfectly

REVERSED

Fit a model to the data and save the model instead

What is model based data reduction?

How well did you know this?

Not at all

Perfectly

REVERSED

Problem of identifying and linking/grouping different representations of the same real-world object

What is entity resolution?

How well did you know this?

Not at all

Perfectly

REVERSED

df.corr()

How do you find the correlation matrix for a dataframe in python?

How well did you know this?

Not at all

Perfectly

REVERSED

global, contextual, collective

What are the three kinds of outliers?

How well did you know this?

Not at all

Perfectly

REVERSED

Don’t assume an a-priori statistical model and determine the model from the input data
e.g. histogram and kernel density estimation

What are non-parametric methods for outlier detection?

How well did you know this?

Not at all

Perfectly

REVERSED

Supervised methods: domain experts examine and label a sample of the underlying data and the sample is used for testing and training. Outlier detection modelled as a classification problem
Unsupervised methods: assume that normal objects are somewhat clustered. Outliers are expected to occur far away from any of the groups of normal objects
Semi-supervised methods: only a small set of the normal or outlier objects are labelled, but most of the data are unlabelled. The labelled normal objects together with unlabelled objects that are close by, can be used to train a model for normal objects

What are the three types of outlier detection methods?

How well did you know this?

Not at all

Perfectly

REVERSED

Simple random sampling may have poor performance in the presence of skew

When does simple random sampling have poor performance?

How well did you know this?

Not at all

Perfectly

REVERSED

checking permitted characters
finding type-mismatched data

What is data validation?

How well did you know this?

Not at all

Perfectly

REVERSED

Reflects the use of the data
Leads to improvements in processes
Measurable (we can define metrics)

What do we need in a definition of data quality? (3)

How well did you know this?

Not at all

Perfectly

REVERSED

Assumes that the normal data is generated by a parametric distribution with the parameter theta

The probability density function of the parametric distribution f(x, gamma) gives the probability that x is generated by the distribution
The smaller this value, the more likely x is an outlier

What are parametric methods for outlier detection?

How well did you know this?

Not at all

Perfectly

REVERSED

#fill each na with the value before it 
data.fillna(method=‘pad') or method=‘ffill’

#fill each na with the value after it 
data.fillna(method=‘bfill’) or method=‘backfill’

#set a limit on the number of forward or backward fills 
data.fillna(method=‘pad’, limit=1)

What are the 2 different methods for filling nas in python?

How well did you know this?

Not at all

Perfectly

REVERSED

Inconsistent: containing discrepancies in codes or names
Intentional: e.g. disguised missing data such as Jan 1st for all birthdays

What makes data “dirty”? (2)

How well did you know this?

Not at all

Perfectly

# REVERSED capitalisation, white space normalisation, correcting typos, replacing abbreviations, variations, nick names

What is data normalisation in text?

# REVERSED - Binning - Histograms - Clustering - Classification (e.g. decision trees) - Correlation

What are discretisation methods? (5)

# REVERSED O(n^2)

What is the time complexity of computing pairwise similarity?

# REVERSED - Divides range into N intervals, each containing approximately the same number of samples - Managing categorical attributes can be tricky

What is equal-depth partitioning for discretisation? What is a problem with it?

# REVERSED Transform the data by moving the decimal points of values of attribute A v’ = v/10j where j is the smallest integer such that max(|v’|) \< 1 e.g. if the maximum absolute value of A is 986, divide each value by 1000 (j=3)

How do you normalise data by decimal scaling?

# REVERSED Global approaches: the reference set contains all other data objects Local approaches: the reference contains a small subset of data objects and there is no assumption on the number of normal mechanisms

What is the difference between global and local approaches to outlier detection

# REVERSED - Accuracy: the data was recorded correctly - Completeness: all relevant data was recorded - Uniqueness: entities are recorded once - Timeliness: the data is kept up to date - Consistency: the data agrees with itself - Believability: how much the data is trusted by users - Interpretability: how easy the data is understood

What is the definition of data quality? (7 parts)

# REVERSED - Divides the range into N intervals of equal size: uniform grid - If A and B are the smallest and largest values of the attribute, the width of the intervals will be W = (B-A)/N - The most straightforward, but outliers may dominate presentation - Skewed data is not handled well

What is equal width partitioning for discretisation? What are the 2 problems with it?

# REVERSED - similarity measures have different scales - pairwise similarity between records is expensive?

What are issues with computing similarity measures? (2)

# REVERSED Given two records, compute a vector of similarity scores for corresponding features -Score can be Boolean (match/mismatch) or a continuous value based on specific similarity measure (distance function)

What is matching features?

# REVERSED - Binning: first sort data and partition into equal frequency (equidepth) bins, then one can smooth by bin means, smooth by bin median, smooth by bin boundaries etc - Regression: smooth by fitting the data into regression functions - Clustering: detect and remove outliers that do not belong to any of the clusters - Combined computer and human inspection: detect suspicious values and check by human

What are 4 ways to handle noisy data?

# REVERSED ``` data.fillna() #inplace=TRUE replaces the values in the original dataframe ```

What is the python code for filling in missing values?

# REVERSED unsupervised, top down splitting method

What type of discretisation method is binning?

# REVERSED Attributes that contain no information that is useful for the data mining task at hand

What are irrelevant attributes?

# REVERSED Object is Oc (or conditional outlier) if it deviates significantly based on a selected context Issue: how to define or formulae meaningful context

What is a contextual outlier and what is the issue with detecting them

# REVERSED - Ignore the tuple: usually done when class label is missing - not effective when the % of missing values is large - Fill in the missing value manually: tedious + inflatable - Fill in the missing value automatically (data imputation) with: a global constant e.g. “unknown” or a new class, the attribute mean, the attribute mean for all samples belonging to the same class, the most probable value found through regression, inference or decision tree

What are 3 ways of handling missing data? (3)

# REVERSED Transform the data from a given range with [minA, maxA] to a new interval [new\_maxA, new\_minA] for a given attribute A v' = (v - minA)/(maxA - minA) \* (newmaxA - newminA) + newminA where v is the current value

What is min-max normalisation?

# REVERSED remove unimportant attributes

What is dimensionality reduction?

# REVERSED assume the normal data is generated by a mixture of normal distributions For any object o in the dataset, the probability that o is generated by a mixture of distributions is the sum of the probability density functions at o Use the EM algorithm to learn the parameters of the data and an object is an outlier if it does not belong to any of the main groups of the data

For multivariate data, how do you overcome the simplified assumption that data is generated by a normal distribution? What method for outlier detection can you use for this new assumption?

# REVERSED - Noise - Duplicate data - Outliers - Unreliable sources - Inconsistent values - Outdated values - Missing values

What are data quality issues? (7)

# REVERSED integrate metadata from different sources

What is schema integration?

# REVERSED jaro\_sim = sm.jaro.Jaro() jaro\_sim.get\_raw\_score(s1, s2)

How do you compute jaro similarity between strings s1 and s2 in python?

# REVERSED Removing irrelevant or redundant attributes

What is attribute subset selection?

# REVERSED - An outlier is an observation which deviates so much from the other observations as to arouse suspicions that it was generated by a different mechanism - Outliers are data or model glitches

What is an outlier?

# REVERSED data set involving two or more attributes or variables

What is multivariate data?

# REVERSED numeric data only

What type of data can you perform principal component analysis on?

# REVERSED Using a histogram: use histogram to graph results as a percentage, a number is in outlier if it falls with a very small percentage of the data Or use a kernel density estimation the probability density distribution of the data. For an object o, the density function f(o) gives the estimated probability that the object is generated by the stochastic process. If f(o) is low the object is likely an outlier

What is a non-parametric method for outlier detection with multivariate data?

# REVERSED A subset of data objects collectively deviate significantly from the whole data set, even if the individual data object may not be outliers Need to have the background knowledge on the relationship among the data objects, such as distance or similarity measure on objects

What are collective outliers?

# REVERSED dividing the range of a continuous attribute into intervals

What is data discretisation?

# REVERSED aff = sm.affine.Affine(...) aff.get\_raw\_score(s1, s2)

How do you compute the affine gap similarity in python?

# REVERSED obtaining a small sample s to represent the whole data set N. choose a representative subset of the data

How do you reduce data by sampling?

# REVERSED Given N data vectors from d-dimensions, find k \<= d principal components that can accurately represent the data. Steps: - Normalise the input data: so that each attribute falls within the same range - Compute k orthonormal (unit) vectors i.e. principal components. These are unit vectors that each point in a direction perpendicular to the others. Each input data (vector) is a linear combination of the k principal components - The principal components are sorted in order of decreasing significance or strength. The principal components serve as new set of axes for the data. The first axis (first ranked principal component) shows the most variance among the data - The components are sorted. Reduce the data dimensionality by eliminating the weak components. Weak components have low variance

What are the steps of principal component analysis?

# REVERSED Tests the hypothesis that attributes A and B are independent based on the chi-squared statistic

What is the chi-squared correlation test for nominal data?

# REVERSED Object is a global outlier (Og) (or point anomaly) if it significantly deviates from the rest of the data set Issue: find an appropriate measure of deviation

What is a global outlier and what is the issue with detecting them?

# REVERSED Assume that the normal data objects belong to large and dense clusters, whereas outliers belong to small or sparse clusters

What is clustering-based outlier detection?

# REVERSED Considering the output of an outlier detection algorithm Labelling approaches: binary output - data objects are labeled either normal or outlier Scoring approaches: continuous output - for each object an outlier score is computed. E.g. the probability for it being an outlier

What is difference between labelling versus scoring for outlier detection:

# REVERSED label encoding, one-hot encoding

What are the names of 2 techniques for turning categorical data into numerical data?

# REVERSED Transform the data by converting the values to a common scale with an average of zero and a standard deviation of one v’ = (v - mean(A))/sd(A)

What is z-score normalisation?

# REVERSED - Simple random sampling: there is an equal probability of selecting any particular item - Simple random sampling without replacement: once an object is selected, it is removed from the population - Simple random sampling with replacement: a selected object is not removed from the population - Cluster sampling: random sampling of clusters - Stratified sampling: partition data set and draw samples from each partition proportionally, i.e. approximately the same percentage of the data. Used in conjunction with skewed data

What are the 5 types of sampling?

# REVERSED data set involving only one attribute or variable

What is univariate data?

# REVERSED - Schema matching: e.g. contact number vs phone - Compound attributes: e.g. address vs street, city, zip

What is schema normalisation?

# REVERSED Assume that the data are normally distributed, learn the parameters from the input data. An object is an outlier if it is more than 3sd from the mean. Ie the z-score (x-mean/sd) has absolute value more than 3

What is the maximum likelihood method for outlier detection?

# REVERSED Blocking: divide the records into blocks, perform pairwise comparison between records in the same block only

How can you reduce the time complexity of pairwise similarity

# REVERSED - Assume that an object is an outlier if the nearest neighbours of the object are far away - Two types of proximity based methods: distance-based and density-based

What are proximity based methods for outlier detection?

# REVERSED - Smoothing: removing noise from the data. includes binning, regression, clustering - Attribute/feature construction: new attributes constructed from the given ones - Aggregation: summary or aggregation operations applied, data cube construction - Normalisation: scaled to fall within a smaller, specified range. Includes min-max normalisation, Z-score normalisation, normalisation by decimal scaling - Data reformatting: e.g. Jack Wilsher -\> Wilsher, J. - Using the same unit: e.g. inches and cm - Discretisation: raw values of numeric data attributes by interval labels or conceptual labels - Concept hierarchy generation: attributes such as street generalised to higher level concepts like city

What are methods for data transformation? (8)

# REVERSED from sklearn.preprocessing import StandardScaler StandardScaler().fit\_transform(df)

How do you normalise data by z-score in python?

# REVERSED Combining data from multiple sources into a coherent data store

What is data integration?

# REVERSED - Smoothing by bin means: each value in a bin is replaced by the mean value of the bin - Smoothing by bin medians: each value in a bin is replaced by the median value of the bin - Smoothing by bin boundary: the minimum and maximum values in a given bin are identified as the bin boundaries. Each bin value is then replaced by the closest boundary value

What ways can you handle noisy data through binning? (3)

# REVERSED - Supervised: given class labels, top down recursive split - Using entropy to determine split point (discretisation point)

What is the classification/decision tree method of discretisation?

# REVERSED ``` split = top down method merge = bottom up method ```

What does split and merge mean in discretisation?

# REVERSED Let o\* be the mean vector for a multivariate dataset. Mahalanobis distance for an object o to o\* is: MDist(o, o\*) = (o-o\*)^TS^-1(o-o\*) where S is the covariance matrix Use the outlier detection technique of Grubbs test on the MDist to detect outliers

What is mahalanobis distance for outlier detection?

# REVERSED data.dropna()

What is the python code for removing missing values?

# REVERSED Redundant attributes can be detected by correlation and covariance analysis

How can you detect/handle redundant data attributes?

# REVERSED Transformations are applied to obtain a reduced or compressed representation of the original data

What is data compression?

# REVERSED Partition data set into clusters based on similarity and store cluster representation (e.g. centroid and diameter) only

How do you reduce data using clustering?

# REVERSED Use a model to summarise the data e.g. linear regression. data points that do not conform to the model are potential outliers

What is a model-based approach to outlier detection?

# REVERSED Assume that the normal data objects are generated by a stochastic process (a generative model) and that data not following the model are outliers. Learn a generative model fitting the given data set, and then identify the objects in low probability regions of the model as outliers

What is a statistical approach to outlier detection?

# REVERSED Obtain a reduced representation of the dataset that is much smaller in volume but yet produces the same (or almost the same) analytical results

What is data reduction?

# REVERSED Judge a point based on its distance to its neighbours Given a radius (r) and a percentage (pi), a datapoint x is considered to be an outlier if the ratio of all other data points that have a distance less than r to x to the total size of the dataset is less than pi

How does the distance-based approach to outlier detection work?

# REVERSED - Unmeasurable: accuracy and completeness are extremely difficult, perhaps impossible to measure - Context independent: no accounting for what is important - Incomplete: what about interpretability, accessibility, metadata, analysis etc - Vague: the previous definition provides no guidance towards practical improvements of the data

What are the problems in the definition of data quality (4)

# REVERSED Must use density. distance based can't detect local outliers

What proximity based approach should you use to detect local outliers?

# REVERSED corr(A,B) = cov(A,B)/sd(A)\*sd(B)

How are correlation and covariance related?

# REVERSED when dimensionality increases, data becomes increasingly spare and density and distance between points becomes less meaningful

What is the curse of dimensionality?

# REVERSED ``` #take sample of 3 rows without replacement: df.sample(3) ``` ``` #take sample of 3 rows with replacement: df.sample(3, replace=True) ```

How do you take a sample of a dataframe with and without replacement in python?

# REVERSED - Principal component analysis (PCA) - Singular value decomposition (SVD) - Feature subset selection, feature creation

What are 3 strategies for dimensionality reduction?

# REVERSED - Divide data into buckets and store average sum for each bucket - Partitioning rules: equal-width (equal bucket range) and equal-frequency (equal depth) (each bucket contains same number of data points)

How do you reduce data with histograms?

# REVERSED contextual attributes define the context, behavioural attributes define the characteristics of the object used in outlier evaluation

What are contextual and behavioural attributes?

# REVERSED df = pd.DataFrame(np.arange(20).reshape(5, 4))

What is the python code to generate a dataframe with 20 elements with 5 rows and 4 columns?

# REVERSED The cost of obtaining a sample is proportional to the size of the sample s, not the size of the dataset N. Therefore sampling complexity is potentially sublinear to the size of the data

What is an advantage of sampling?

# REVERSED A function that maps the entires set of values of a given attribute to a new set of replacement values such that each old value can be identified with one of the new value

What is data transformation?

# REVERSED - Modelling normal objects and outliers properly - Application-specific outlier detection - Handling noise in outlier detection - Understandability - A data set may have multiple types of outlier - One object may belong to more than one type of outlier

What are challenges of outlier detection? (6)

# REVERSED data[“column1”].fillna(data.groupby(“column2)[“column1”].transform(“mean”))

What is the code in python to: fill nas in column 1 with mean values of column 1 grouped by column 2

# REVERSED - Data can be aggregated for example if you have the sales for each quarter, create a new variable with yearly sales. the resulting dataset is smaller - Data cubes store multidimensional aggregated information

What is data cube aggregation?

# REVERSED Business understanding Data understanding Data preparation Modelling Evaluation Deployment

What are the steps of CRISP-DM (Cross industry processing for data mining) (6)

# REVERSED O(k(n/k)^2)

What is the time complexity of doing pairwise similarity in blocks with k blocks and block size n/k?

# REVERSED The closest cluster is far from x

What does a low local reachability distance mean?

# REVERSED np.add(A, B)

How do you add two lists A and B by element addition using numpy as np in python?

# REVERSED - Too many bins, won’t smooth data, will keep the noise, lot of computation required - Too little bins, hide a lot of details in the data

What are the disadvantages of too many or too little bin numbers for smoothing data?

# REVERSED - Find a projection that captures the largest amount of variation in data - We find the eigenvectors of the covariance matrix, and these eigenvectors define the new space

What is principal component analysis?

# REVERSED - Difference between numerical values - Jaro for comparing names - Edit distance for typos - Phonetic-based - Jaccard for sets - Cosine for vectors

What similarity measures can be used for matching features? (6)

# REVERSED np.mean(data)

How do you get summary statistics such as mean using numpy as np in python?

# REVERSED - Replace the original data volume by alternative, smaller forms of data representation - Includes modelling, histograms, clustering, sampling and data cube aggregation

What is numerosity reduction?

Week 5: Data Preparation Flashcards

(106 cards)