Data Mining Steps1. Data Cleaning  Removal of noise and inconsistent records  2. Data Integration  Combing multiple sources  3. Data Selection  Only data relevant for the task are retrieved from the database  4. Data Transformation  Converting data into a form more appropriate for mining  5. Data Mining  Application of intelligent methods to extract data patterns  6. Model Evaluation  Identification of truly interesting patterns representing knowledge  7. Knowledge Presentation  Visualization or other knowledge presentation techniques 
Data mining could also be called Knowledge Discovery in Databases (see kdnuggets.com) Types of AttributesNomial  e.g., ID numbers, eye color, zip codes  Ordinal  e.g., rankings, grades, height  Interval  e.g., calendar dates, temperatures  Ratio  e.g., length, time, counts 
Distance MeasuresManhattan = City Block
Jaccard coefficient, Hamming, Cosine are a similarity / dissimilarity measures Measures of Node ImpurityModel EvaluationKappa = (observed agreement  chance agreement) / (1 chance agreement)
Kappa = (Dreal – Drandom) / (Dperfect – Drandom), where D indicates the sum of values in diagonal of the confusion matrix KNearest Neighbor* Compute distance between two points
* Determine the class from nearest neighbor list
* Take the majority vote of class labels
among the knearest neighbors
* Weigh the vote according to distance
* weight factor, w = 1 / d^2

Rulebased ClassificationClassify records by using a collection of
“if…then…” rules
Rule: (Condition) > y
where:
* Condition is a conjunction of attributes
* y is the class label
LHS: rule antecedent or condition
RHS: rule consequent
Examples of classification rules:
(Blood Type=Warm) ^ (Lay Eggs=Yes) > Birds
(Taxable Income < 50K) ^ (Refund=Yes) > Evade=No
Sequential covering is a rulebased classifier. 
Bayesian Classificationp(a,b) is the probability that both a and b happen.
p(ab) is the probability that a happens, knowing that b has already happened. TermsAssociation Analysis  MinApriori, LIFT, Simpson's Paradox, Antimonotone property  Ensemble Methods  Staking, Random Forest  Decision Trees  C4.5, Pessimistic estimate, Occam's Razor, Hunt's Algorithm  Model Evaluation  Crossvalidation, Bootstrap, Leaveone out (CV), Misclassification error rate, Repeated holdout, Stratification  Bayes  Probabilistic classifier  Data Visualization  Chernoff faces, Data cube, Percentile plots, Parallel coordinates  Nonlinear Dimensionality Reduction  Principal components, ISOMAP, Multidimensional scaling 
  Ensemble TechniquesManipulate training data: bagging and boosting ensemble of “experts”, each specializing on different portions of the instance space
Manipulate output values: errorcorrecting output coding (ensemble of “experts”, each predicting 1 bit of the {multibit} full class label)
Methods: BAGGing, Boosting, AdaBoost
Apriori AlgorithmLet k=1
Generate frequent itemsets of length 1
Repeat until no new frequent itemsets are identified
Generate length (k+1) candidate itemsets from
length k frequent itemsets
Prune candidate itemsets containing subsets
of length k that are infrequent
Count the support of each candidate by
scanning the DB
Eliminate candidates that are infrequent,
leaving only those that are frequent

Kmeans ClusteringSelect K points as the initial centroids
repeat
Form K Clusters by assigning all points to the closest centroid
Recompute the centroid of each cluster
until the centroids don't change

Closeness is measured by distance (e.g., Euclidean), similarity (e.g., Cosine), correlation.
Centroid is typically the mean of the points in the cluster Hierarchical ClusteringSingleLink or MIN
Similarity of two clusters is based on the two most similar (closest / minimum) points in the different clusters
Determined by one pair of points, i.e., by one link in the proximity graph.
Complete or MAX
Similarity of two clusters is based on the two least similar (most distant, maximum) points in the different clusters
Determined by all pairs of points in the two clusters
Group Average
Proximity of two clusters is the average of pairwise proximity between points in the two clusters 
Agglomerative clustering starts with points as individual clusters and merges closest clusters until only one cluster left.
Divisive clustering starts with one, allinclusive cluster and splits a cluster until each cluster only has one point. Dendrogram ExampleDataset: {7, 10, 20, 28, 35} DensityBased Clusteringcurrent_cluster_label < 1
for all core points do
if the core point has no cluster label then
current_cluster_label < current_cluster_label +1
Label the current core point with the cluster label
end if
for all points in the Epsneighborhood, except ith the point itself do
if the point does not have a cluster label then
Label the point with cluster label
end if
end for
end for

DBSCAN is a popular algorithm
Density = number of points within a specified radius (Eps)
A point is a core point if it has more than a specified number of points (MinPts) within Eps
These are points that are at the interior of a cluster
A border point has fewer than MinPts within Eps, but is in the neighborhood of a core point
A noise point is any point that is not a core point or a border point Other Clustering MethodsFuzzy is a partitional clustering method. Fuzzy clustering (also referred to as soft clustering) is a form of clustering in that each data point can belong to more than one cluster.
Graphbased methods: JarvisPatrick, SharedNear Neighbor (SNN, Density), Chameleon
Modelbased methods: ExpectationMaximization 
Regression Analysis* Linear Regression
 Least squares
* Subset selection
* Stepwise selection
* Regularized regression
 Ridge
 Lasso
 Elastic Net 
Anomaly DetectionAnomaly is a pattern in the data that does not conform to the expected behavior (e.g., outliers, exceptions, peculiarities, surprise)
Types of Anomaly
Point: An individual data instance is anomalous w.r.t. the data
Contextual: An individual data instance is anomalous within a context
Collective: A collection of related data instances is anomalous
Approaches
* Graphical (e.g., boxplots, scatter plots)
* Statistical (e.g., normal distribution, likelihood)
 Parametric Techniques
 Nonparametric Techniques
* Distance (e.g., nearestneighbor, density, clustering) 
Local outlier factor (LOF) is a densitybased distance approach
Mahalanobis Distance is a clusteringbased distance approach 
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