Description

PrefixSpan algorithm is used to mine frequent sequential patterns. The PrefixSpan algorithm is described in J. Pei, et al., Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach

Parameters

Name Description Type Required？ Default Value
itemsCol Column name of transaction items String ✓
minSupportCount Minimum support count Integer -1
minSupportPercent Minimum support percent Double 0.02
minConfidence Minimum confidence Double 0.05
maxPatternLength Maximum frequent pattern length Integer 10

Script Example

Code

  1. data = np.array([
  2.     ["a;a,b,c;a,c;d;c,f"],
  3.     ["a,d;c;b,c;a,e"],
  4.     ["e,f;a,b;d,f;c;b"],
  5.     ["e;g;a,f;c;b;c"],
  6. ])
  8. df_data = pd.DataFrame({
  9.     "sequence": data[:, 0],
  10. })
  12. data = dataframeToOperator(df_data, schemaStr='sequence string', op_type='batch')
  14. prefixSpan = PrefixSpanBatchOp() \
  15.     .setItemsCol("sequence") \
  16.     .setMinSupportCount(3)
  18. prefixSpan.linkFrom(data)
  20. prefixSpan.print()
  21. prefixSpan.getSideOutput(0).print()

输入说明：一个sequence由多个element组成，element之间用分号分隔；一个element由多个item组成，item间用逗号分隔。

Results

Output

  1.     itemset  supportcount  itemcount
  2. 0        a             4          1
  3. 1      a;c             4          2
  4. 2    a;c;c             3          3
  5. 3    a;c;b             3          3
  6. 4      a;b             4          2
  7. 5        b             4          1
  8. 6      b;c             3          2
  9. 7        c             4          1
  10. 8      c;c             3          2
  11. 9      c;b             3          2
  12. 10       d             3          1
  13. 11     d;c             3          2
  14. 12       e             3          1
  15. 13       f             3          1

Output

  1.      rule  chain_length  support  confidence  transaction_count
  2. 0    a=>c             2     1.00        1.00                  4
  3. 1  a;c=>c             3     0.75        0.75                  3
  4. 2  a;c=>b             3     0.75        0.75                  3
  5. 3    a=>b             2     1.00        1.00                  4
  6. 4    b=>c             2     0.75        0.75                  3
  7. 5    c=>c             2     0.75        0.75                  3
  8. 6    c=>b             2     0.75        0.75                  3
  9. 7    d=>c             2     0.75        1.00                  3