Advanced Pattern Mining

Advanced Pattern Mining

Advanced pattern mining builds on foundational methods to handle:

• Complex data types: Sequences, graphs, streams, multi-dimensional data.
• Constraints: User-defined rules to focus on meaningful patterns.
• Scalability: Large-scale or dynamic datasets.
• Specialized patterns: Beyond simple itemsets (e.g., maximal, closed, or rare patterns).

1. Advanced Pattern Types

Beyond frequent itemsets, advanced mining uncovers richer patterns:

• Maximal Frequent Itemsets (MFI):

  • An itemset is maximal if it’s frequent and none of its supersets are frequent.
  • Example: If {A, B, C} is frequent but {A, B, C, D} isn’t, {A, B, C} is maximal.
  • Benefit: Reduces redundancy (fewer patterns to store).

• Closed Frequent Itemsets:

  • An itemset is closed if it’s frequent and no superset has the same support.
  • Example: If {A, B} has support 50% and {A, B, C} also has 50%, only {A, B, C} is closed.
  • Benefit: Compact representation without losing information.

• Rare or Infrequent Patterns:

  • Focus on patterns with low support but high significance (e.g., fraud detection).
  • Challenge: Traditional algorithms like Apriori miss these due to high min-support thresholds.

• High-Utility Itemsets (HUI):

  • Considers item importance (e.g., profit, cost) beyond frequency.
  • Example: {diamond ring} may appear rarely but has high utility (profit).
  • Algorithms: HUI-Miner, FHM.

2. Advanced Algorithms

To mine these patterns efficiently, we go beyond Apriori and FP-Growth:

• PrefixSpan (Sequential Pattern Mining):

  • Mines patterns in ordered sequences (e.g., customer purchase sequences: {milk} → {bread} → {butter}).
  • How it works: Projects the database into smaller prefix-based subsets, avoiding candidate generation.
  • Use case: Web clickstream analysis, DNA sequence mining.

• CM-SPADE (Sequential Mining with Constraints):

  • Extends sequential mining with user constraints (e.g., length, time gaps).
  • Faster than PrefixSpan for constrained problems.

• Graph Pattern Mining (e.g., gSpan):

  • Mines frequent subgraphs in graph datasets (e.g., social networks, chemical compounds).
  • How it works: Uses a depth-first search to explore canonical graph representations.
  • Use case: Drug discovery (finding common molecular structures).

• Vertical Data Mining (ECLAT):

  • Uses a vertical database format (item-to-transaction mapping) instead of horizontal (transaction-to-item).
  • Benefit: Faster intersection operations, memory-efficient for sparse data.

3. Constraint-Based Mining

Real-world applications often need specific patterns, not just frequent ones. Constraints refine the search:

• Monotonic Constraints: If a pattern satisfies it, all supersets do too (e.g., support ≥ 5%).
• Anti-Monotonic Constraints: If a pattern violates it, all supersets do too (e.g., cost ≤ $100).
• Succinct Constraints: Directly expressible (e.g., item = “milk”).
• Example: Find rules where {milk} appears on the left-hand side with confidence > 70%.

Algorithms like Apriori can be adapted to push constraints deep into the mining process, pruning irrelevant patterns early.

4. Mining Dynamic and Streaming Data

Static datasets are one thing, but real-time data (e.g., IoT, stock trades) requires advanced techniques:

• Incremental Mining:

  • Updates patterns as new data arrives without re-mining everything.
  • Example: IncSpan for sequences, UF-Growth for FP-Trees.

• Stream Mining:

  • Handles unbounded data with sliding windows or decay factors.
  • Algorithms: Lossy Counting, Moment (for closed patterns).
  • Challenge: Limited memory, one-pass processing.

5. Multi-Dimensional and Multi-Level Mining

Real data often has attributes beyond items (e.g., time, location, category):

• Multi-Dimensional Patterns:

  • Example: {milk, bread} in {summer, NYC} with support 30%.
  • Approach: Treat dimensions as items or use cube-based mining (e.g., BUC algorithm).

• Multi-Level Patterns:

  • Mines at different abstraction levels (e.g., {milk} → {bread} vs. {dairy} → {bakery}).
  • Uses concept hierarchies to generalize or specialize patterns.

6. Practical Example

Imagine an e-commerce dataset:

• Basic Mining: {laptop, charger} → {mouse} (support 10%, confidence 80%).
• Advanced Mining:
  • Maximal: {laptop, charger, mouse} is maximal if no larger set is frequent.
  • Sequential: {laptop} → {charger} → {mouse} over three days.
  • High-Utility: {laptop, warranty} has low frequency but high profit.
  • Constraint: Rules where total cost > $500.

Challenges in Advanced Mining

• Scalability: More complex patterns mean higher computational cost.
• Interpretability: Too many patterns can overwhelm users.
• Noise: Real data is messy, requiring robust preprocessing.