Quantitative Discriminant Rule

• general form: $\forall X,$target_class$(X)\Leftrightarrow$ contition$_1(X)[t:w_1,d:\omega_1]\vee\cdots\vee$ condition$_n(X)[t: w_n, d:\omega_n]$
• Discriminant rule: sufficient condition of the target class
  • d-weight = $\frac{\text{count}(q_a\in C_{\text{target}})}{\sum_{i=1}^N\text{count}(q_a\in C_i)}$
  • d-weight: discriminability of each disjunct in the rule
  • important
• Characteristic rule: necessary condition
  • $\sum t_i=100%$

Association

• Association rules: $A\Rightarrow B$[support, condifence]

  • Support

    $$\text{support}(A\Rightarrow B)=P(A,B)=\frac{\text{# of tuples includes }A,B}{\text{# of total tuples}}$$

  • Confidence (t-weight)

    $$\text{confidence}(A\Rightarrow B)=P(B|A)=\frac{\text{# of tuples includes }A,B}{\text{# of tuples includes }A}$$

• strong: support > min_sup, confidence > min_conf

• k-itemset: contains k items

• frequency: support count

• frequent itemset: support > min_sup

Apriori

• Two-step process
  • find all frequent itemsets
  • generate strong association rules (easy)
• INSIGHT: All non-empty subsets of a frequent itemset must also be frequent
• frequent $k$-itemset: $L_k$
• Join: $C_{k+1} = L_k\Join L_k={A\Join B|A,B\in L_k,|A\cap B|=k-1}$
• Prune: determine candidates in $C_{k+1}$ to get $L_{k+1}$
• ordering $L_k$

Apriori variant

• AIS: for each tuple, expand $L_k$ by adding other items contained in the tuple to generate $C_{k+1}$

• AprioriTid: calculate support in $\overline{C_k}$

• DHP (Direct Hashing and Pruning)

  • INSIGHT: the processes in the initial iterations of Apriori dominates the total execution cost
  • Knowledge 1: Any member of a candidate frequent itemset must be hashed into a bucket whose count $\geq$ min_sup
  • Knowledge 2: Any tuple useful in determining $L_{k+1}$ must contain at least $k+1$ sets in $C_k$
  • Knowledge 3: For any items contained in a tuple, if it is useful in determining $L_{k+1}$, it must appear in at least $k$ sets in $C_k$
  • Knowledge 4: For any items contained in a tuple, if it is useful in determining $L_{k+1}$ it must appear in at least one (k+1)-itemset whose k-itemsets are all candidate frequent k-itemsets

• Improvement

  • partitioning
  • sampling
  • transaction reduction

FP-growth

• Mining from FP-tree
• FP-tree construction
  • scan DB once, find frequent 1-itemset
  • sort frequent items in frequency descending order to get F-list
  • scan DB again construct FP-tree
  • for each frequent item in reverse frequency, construct its conditional pattern-base and conditional FP-tree
  • repeat on newly created conditional FP-tree until empty

Others

• closed frequent itemset
  • closed: no proper super-itemset $Y$ such that $Y$ has the same support count as $X$ in $S$
• Maximal frequent itemset: if $X$ is frequent, and there exists no proper super-itemset $Y$ such that $Y$ is frequent in $S$
• Multilevel association rules: Rules generated from association rule mining with concept hierarchies
  • same min_sup for all levels
  • level-by-level independent
  • level-cross filtering by $k$-itemsets
  • Level-crossfilteringbysingleitem
  • Controlled level-cross filtering by single item
• Cross-level association rules
• Quantitative Association rules
  • Step 1. Binning
  • Step 2. Finding frequent predicatesets
  • Step 3. Clustering association rules
• Distance-based association rules
• Criticism: Strong association rules may not be interesting
• Correlation analysis
  • Lift: $\frac{P(AB)}{P(A)P(B)}$