data mining 2

Important Characteristics of Structured Data

• Dimensionality Curse (text data, many keywords)
• Sparsity of the matrix (only presence counts)
• Resolution (patterns depend on the scale, macro, micro)

Document Data: each document becomes a 'term' vector

• each term is a component of the vector
• the value of each component is the number of times the corresponding term occurs in the document

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Transaction Data
A special type of record data, where

• each record (transaction) involves a set of items.
• For example, consider a grocery store. The set of products purchased by a customer during one shopping trip constitute a transaction, while the individual products that were purchased are the items.

Graph Data
Examples: generic graph, a molecule and webpages.

Ordered Data
Sequences of transactions: Genomic sequence data

Data Quality

• Poor data quality negatively affects many data processing efforts
• Data mining example: a classification model for detecting people who are loan risks is built using poor data
  • Some credit-worthy candidates are denied loans
  • More loans are given to individuals that default
    Examples of data quality problems:
• Noise and outliers (noisy data is wrong, outlier data exists)
• Missing values (incoming data missing)
• Duplicate data (increase the weight of some attributes)

Missing Values
Handling missing values

• Eliminate data objects
• Estimate missing values
• Ignore the missing value during analysis

Duplicate Data
Data set may include data objects that are duplicates, or almost duplicates of one another

• Major issue when merging data from heterogeous sources
  Data cleaning
• Process of dealing with duplicate data issues

Aggregation
Combining two or more attributes (or objects) into a single attribute (or object)
Purpose

• Data reduction
• Change of scale
• More 'stable' data (reduce the variance)

Sampling
Sampling is the main technique employed for data selection. It is often used for both the preliminary investigation of the data and the final data analysis.
Statisticians sample because obtaining the entire set of data of interest is too expensive or time consuming.
The key principle for effective sampling is the following:

• Using a sample will work almost as well as using the entire data sets, if the sample is representative
• A sample is representative if it has approximately the same property (of interest) as the original set of data

Curse of Dimensionality

• When dimensionality increases, data becomes increasingly sparse in the space that it occupies.
• Definitions of density and distance between points, which is critical for clustering and outlier detection, become less meaningful
  Purpose:
• Avoid curse of dimensionality
• Reduce amount of time and memory required by data mining algorithms
• Allow data to be more easily visualized
• May help to eliminate irrelevant features or reduce noise
  Techniques
• Principal Components Analysis (PCA)
• Singular Value Decomposition
• Others: supervised and non-linear techniques

Dimensionality Reduction: PCA
Goal is to find a projection that captures the largest amount of variation in data

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Feature Subset Selection
Another way to reduce dimensionality of data
Redundant features

• Duplicate much or all of the information contained in one or more other attributes
• Example: purchase price of a product and the amount of sales tax paid
  Irrelevant features
• Contain no information that is useful for the data mining task at hand
• Example: students' ID is often irrelevant to the task of predicting students' GPA
  Many techniques developed, especially for classification

Feature Creation
Create new attributes that can capture the important information in a data set much more efficiently than the original attributes
Three general methodologies:

• Feature extraction
  • Example: extracting edges from images
• Feature construction
  • Example: dividing mass by volume to get density
• Mapping data to new space
  • Example: Fourier and wavelet analysis
    Mapping Data to a New Space

• Fourier and wavelet transform

  
  
  

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