Characteristic identifier scoring and clustering for email classification
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Characteristic Identifier Scoring and Clustering for Email Classification. By Mahesh Kumar Chhaparia. Email Clustering. Given a set of unclassified emails, the objective is to produce high purity clusters keeping the training requirements low. Outline:

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Characteristic identifier scoring and clustering for email classification l.jpg

Characteristic Identifier Scoring and Clustering for Email Classification

By

Mahesh Kumar Chhaparia


Email clustering l.jpg
Email Clustering Classification

  • Given a set of unclassified emails, the objective is to produce high purity clusters keeping the training requirements low.

  • Outline:

    • Characteristic Identifier Scoring and Clustering (CISC),

      • Identifier Set

      • Scoring

      • Clustering

      • Directed Training

    • Comparison of CISC with some of the traditional ideas in email clustering

    • Comparison of CISC with POPFile (Naïve-Bayes classifier),

    • Caveats

    • Conclusion


Evaluation l.jpg
Evaluation Classification

  • Evaluation on Enron Email Dataset for the following users (purity measured w.r.t the grouping already available):


Cisc identifier set l.jpg
CISC: Identifier Set Classification

  • Sender and Recipients

  • Words from the subject starting with uppercase

  • Tokens from the message body

    • Word sequences with each word starting in uppercase (length [2,5] only) split about stopwords (excluding them)

    • Acronyms (length [2,5] only)

    • Words followed by an apostrophe and ‘s’ e.g. TW’s extracted to TW

    • Words or phrases in quotes e.g. “Trans Western”

    • Words where any character (excluding first is in uppercase) e.g. eSpeak, ThinkBank etc.


Cisc scoring l.jpg
CISC: Scoring Classification

  • Sender:

    • Initial idea: generate clusters of email addresses with frequency of communication above some threshold,

      • (+) Identifies “good” clusters of communication

      • (-) Difficult to score when an email has addresses spread across more than one cluster

      • (-) Fixed partitioning and difficult to update


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CISC: Scoring (Contd…) Classification

  • Sender:

    • Need a notion of soft clustering with both recipients and content

    • Generate a measure of its non-variability with respect to the addresses it co-occurs with or the content it discusses in emails

    • Example:

      • 1  {2,3} {3,4} {2,3,4} in Folder 1

      • 2  {1} {3} {4} {1} {3} {1,3} in Folder 2

      • Emphasizes social clusters {1,2,3} {1,3,4}

      • Classify 2  {1,3,4}

        • Traditionally: Folder 2 (address frequency based)

        • CISC: Folder 1 (social cluster based)

        • Difficult to say upfront which is better !

        • Efficacy discussed later


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CISC: Scoring (Contd…) Classification

  • Words or Phrases:

    • Generate a measure of its importance

    • Using context captured through the co-occurring text

    • Sample scenarios for score generation:

      • Different functional groups in a company mentioning “Conference Room”  Low score

      • A single shipment discussion for company “CERN”  High score

      • Several different topic discussions (financial, operational etc.) for company “TW”  Low score

  • Clustering: Pair with highest similarity message and merge clusters sharing atleast one message to produce disjoint clusters

  • Directed Training:

    • For each cluster, identify a message likely to belong to majority class

    • Suggest the user to classify this message


Efficacy of tf idf cosine similarity l.jpg
Efficacy of TF-IDF Cosine Similarity Classification

  • Clustering using the traditional TF-IDF cosine similarity measure for emails not very effective !

    Note:

  • Both TF-IDF and CISC figures with only word and phrase tokens

  • Number of clusters is different in both cases, but the purity figures indicate the discriminative capability of the respective algorithms



Cisc vs popfile l.jpg
CISC vs. POPFile Classification

  • Results

  • Purity may sometimes (marginally) decrease with increasing training set in POPFile !


Conclusion l.jpg
Conclusion Classification

  • Given a set of unclassified emails, the proposed strategy obtains higher clustering purity with lower training requirements than POPFile and TF-IDF based method.

  • Key differentiators:

    • Incorporates a combination of communication cluster and content variability based scoring for senders instead of the usual tf-idf scoring or naïve-bayes word model (POPFile),

    • Picks a set of high-selectivity features for final message similarity model than retaining most content of messages (i.e. all non-stopwords),

    • Observes and uses the fact that any email in a class may be “close” to only a small number of emails than to all in that class,

    • Finally, helps lower training requirements through “directed training” than indiscriminate training over as many emails as possible.


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Future Work Classification

  • Design and evaluation for non-corporate datasets

  • Tuning of message similarity scoring

    • Different weights for the score components

    • Different range normalization for different components to boost proportionally

    • Test feature score proportional to its length

  • Richer feature set

    • Phrases following ‘the’

    • Test with substring-free collection e.g. “TW Capacity Release Report” and “TW” are replaced with “Capacity Release Report” and “TW”

  • Hierarchical word scoring to change granularity of clustering

  • Online classification using training directed feature extraction

  • Merging high purity clusters effectively to further reduce training requirements


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Q &A Classification