RK Project Zone

Itemset Mining (Frequent Itemsets, Rare Itemsets, etc.)

• Question Answer 1 : Mining Frequent Itemsets by Using the Apriori Algorithm
• Question Answer 2 : Mining Frequent Itemsets by Using the AprioriTID Algorithm
• Question Answer 3 : Mining Frequent Itemsets by Using the FP-Growth Algorithm
• Question Answer 4 : Mining Frequent Itemsets by Using the Relim Algorithm
• Question Answer 5 : Mining Frequent Itemsets by Using the Eclat / dEclat Algorithm
• Question Answer 6 : Mining Frequent Itemsets by Using the H-Mine Algorithm
• Question Answer 7 : Mining Frequent Itemsets by Using the FIN Algorithm
• Question Answer 8 : Mining Frequent Itemsets by Using the PrePost / PrePost+ Algorithm
• Question Answer 9 : Mining Frequent Itemsets by Using the LCMFreq Algorithm
• Question Answer 10 : Mining Frequent Closed Itemsets Using the AprioriClose Algorithm
• Question Answer 11 : Mining Frequent Closed Itemsets Using the DCI_Closed Algorithm
• Question Answer 12 : Mining Frequent Closed Itemsets Using the Charm / dCharm Algorithm
• Question Answer 13 : Mining Frequent Closed Itemsets Using the LCM Algorithm
• Question Answer 14 : Mining Frequent Closed Itemsets Using the FPClose Algorithm
• Question Answer 15 : Mining Frequent Maximal Itemsets Using the FPMax Algorithm
• Question Answer 16 : Mining Frequent Maximal Itemsets Using the Charm-MFI Algorithm
• Question Answer 17 : Mining Frequent Generator Itemsets Using the DefMe Algorithm
• Question Answer 18 : Mining Frequent Itemsets and Identify the Generators Using the Pascal Algorithm
• Question Answer 19 : Mining Frequent Closed Itemsets and Minimal Generators Using the Zart Algorithm
• Question Answer 20 : Mining Minimal Rare Itemsets Using the AprioriRare Algorithm
• Question Answer 21 : Mining Perfectly Rare Itemsets Using the AprioriInverse Algorithm
• Question Answer 22 : Mining Rare Correlated Itemsets Using the CORI Algorithm
• Question Answer 23 : Mining Closed Itemsets from a Data Stream Using the CloStream Algorithm (source code version only)
• Question Answer 24 : Mining Recent Frequent Itemsets from a Data Stream Using the estDec Algorithm (source code version only)
• Question Answer 25 : Mining Recent Frequent Itemsets from a Data Stream Using the estDec+ Algorithm (source code version only)
• Question Answer 26 : Mining Frequent Itemsets from Uncertain Data with the UApriori Algorithm
• Question Answer 27 : Mining Erasable Itemsets from a Product Database with the VME algorithm
• Question Answer 28 : Building, updating incrementally and using an Itemset-Tree to generate targeted frequent itemsets and association rules (source code version only)
• Question Answer 29 : Building, updating incrementally and using a Memory-Efficient Itemset-Tree to generate targeted frequent itemsets and association rules (source code version only)
• Question Answer 30 : Mining Frequent Itemsets with Multiple Support Thresholds Using the MSApriori Algorithm
• Question Answer 31 : Mining Frequent Itemsets with Multiple Support Thresholds Using the CFPGrowth++ Algorithm
• Question Answer 32 : Mining Fuzzy Frequent Itemsets in a quantitatve transaction database using the FFI-Miner algorithm
• Question Answer 33 : Mining Multiple Fuzzy Frequent Itemsets in a quantitatve transaction database using the MFFI-Miner algorithm

High-Utility Pattern Mining

• Question Answer 34 : Mining High-Utility Itemsets from a Transaction Database with Utility Information using the Two-Phase Algorithm
• Question Answer 35 : Mining High-Utility Itemsets from a Transaction Database with Utility Information using the FHM Algorithm
• Question Answer 36 : Mining High-Utility Itemsets from a Transaction Database with Utility Information using the EFIM Algorithm
• Question Answer 37 : Mining High-Utility Itemsets from a Transaction Database with Utility Information using the HUI-Miner Algorithm
• Question Answer 38 : Mining High-Utility Itemsets from a Transaction Database with Utility Information using the HUP-Miner Algorithm
• Question Answer 39 : Mining High-Utility Itemsets from a Transaction Database with Utility Information using the UP-Growth / UP-Growth+ Algorithm
• Question Answer 40 : Mining High-Utility Itemsets from a Transaction Database with Utility Information using the IHUP Algorithm
• Question Answer 41 : Mining High-Utility Itemsets from a Transaction Database with Utility Information using the mHUIMiner Algorithm
• Question Answer 42 : Mining High-Utility Itemsets from a Transaction Database with Utility Information using the d2HUP Algorithm
• Question Answer 43 : Mining High-Utility Itemsets from a Transaction Database with Utility Information while considering Length Constraints, using the FHM+ algorithm
• Question Answer 44 : Mining Correlated High-Utility Itemsets in a Transaction Database with Utility Information using the FCHM algorithm
• Question Answer 45 : Mining Frequent High-Utility Itemsets from a Transaction Database with Utility Information using the FHMFreq Algorithm
• Question Answer 46 : Mining High-Utility Itemsets from a Transaction Database with Positive or Negative Unit Profit using the FHN Algorithm
• Question Answer 47 : Mining High-Utility Itemsets from a Transaction Database with Positive or Negative Unit Profit using the HUINIV-Mine Algorithm
• Question Answer 48 : Mining On-Shelf High-Utility Itemsets from a Transaction Database using the FOSHU Algorithm
• Question Answer 49 : Mining On-Shelf High-Utility Itemsets from a Transaction Database using the TS-HOUN Algorithm
• Question Answer 50 : Incremental High-Utility Itemset Mining in a Transaction Database with utility information using the EIHI Algorithm (source code version only)
• Question Answer 51 : Incremental High-Utility Itemset Mining in a Transaction Database with utility information using the HUI-LIST-INS Algorithm (source code version only)
• Question Answer 52 : Mining Closed High-Utility Itemsets from a transaction database with utility information using the EFIM-Closed Algorithm
• Question Answer 53 : Mining Closed High-Utility Itemsets from a transaction database with utility information using the CHUI-Miner Algorithm
• Question Answer 54 : Mining Generators of High-Utility Itemsets from a transaction database with utility information using the GHUI-Miner Algorithm
• Question Answer 55 : Mining High-Utility Generator Itemsets from a transaction database with utility information using the HUG-Miner Algorithm
• Question Answer 56 : Mining Minimal High-Utility Itemsets from a transaction database with utility information using the MinFHM Algorithm
• Question Answer 57 : Mining Skyline High-Utility Itemsets in a transaction database with utility information using the SkyMine Algorithm
• Question Answer 58 : Mining High-Utility Sequential Rules from a Sequence Database with utility information using the HUSRM Algorithm
• Question Answer 59 : Mining High-Utility Sequential Patterns from a Sequence Database with utility information using the USPAN Algorithm
• Question Answer 60 : Mining High-Utility Itemsets based on Particle Swarm Optimization with the HUIM-BPSO algorithm
• Question Answer 61 : Mining High-Utility Itemsets based on Particle Swarm Optimization with the HUIM-BPSO-tree algorithm
• Question Answer 62 : Discovery of High Utility Itemsets Using a Genetic Algorithm with the HUIM-GA algorithm
• Question Answer 63 : Discovery of High Utility Itemsets Using a Genetic Algorithm with the HUIM-GA-tree algorithm
• Question Answer 64 : Mining Skyline Frequent-Utility Patterns using the SFUPMinerUemax algorithm
• Question Answer 65 : Mining High Average-Utility Itemsets in a Transaction Database with Utility Information using the HAUI-Miner Algorithm
• Question Answer 66 : Mining High Average-Utility Itemsets with Multiple Thresholds in a Transaction Database using the HAUI-MMAU Algorithm

Association Rule Mining

• Question Answer 67 : Mining All Association Rules
• Question Answer 68 : Mining All Association Rules with the lift measure
• Question Answer 69 : Mining All Association Rules using the GCD algorithm
• Question Answer 70 : Mining the IGB basis of Association Rules
• Question Answer 71 : Mining Perfectly Sporadic Association Rules
• Question Answer 72 : Mining Closed Association Rules
• Question Answer 73 : Mining Minimal Non Redundant Association Rules
• Question Answer 74 : Mining Indirect Association Rules with the INDIRECT algorithm
• Question Answer 75 : Hiding Sensitive Association Rules with the FHSAR algorithm.
• Question Answer 76 : Mining the Top-K Association Rules
• Question Answer 77 : Mining the Top-K Non-Redundant Association Rules

Clustering

• Question Answer 78 : Clustering using the K-Means algorithm
• Question Answer 79 : Clustering using the DBScan algorithm
• Question Answer 80 : Using Optics to extract a cluster-ordering of points and DB-Scan style clusters
• Question Answer 81 : Clustering using the Bisecting K-Means algorithm
• Question Answer 82 : Clustering using a Hierarchical Clustering algorithm
• Question Answer 83 : Visualizing clusters using the Cluster Viewer
• Question Answer 84 : Visualizing clusters using the Instance Viewer

Sequential Pattern Mining

• Question Answer 85 : Mining Frequent Sequential Patterns Using the PrefixSpan Algorithm
• Question Answer 86 : Mining Frequent Sequential Patterns Using the GSP Algorithm
• Question Answer 87 : Mining Frequent Sequential Patterns Using the SPADE Algorithm
• Question Answer 88 : Mining Frequent Sequential Patterns Using the CM-SPADE Algorithm
• Question Answer 89 : Mining Frequent Sequential Patterns Using the SPAM Algorithm
• Question Answer 90 : Mining Frequent Sequential Patterns Using the CM-SPAM Algorithm
• Question Answer 91 : Mining Frequent Sequential Patterns Using the LAPIN Algorithm
• Question Answer 92 : Mining Frequent Closed Sequential Patterns Using the ClaSP Algorithm
• Question Answer 93 : Mining Frequent Closed Sequential Patterns Using the CM-ClaSP Algorithm
• Question Answer 94 : Mining Frequent Closed Sequential Patterns Using the CloSpan Algorithm
• Question Answer 95 : Mining Frequent Closed Sequential Patterns Using the BIDE+ Algorithm
• Question Answer 96 : Mining Frequent Closed Sequential Patterns by Post-Processing using SPAM or PrefixSpan
• Question Answer 97 : Mining Frequent Maximal Sequential Patterns Using the MaxSP Algorithm
• Question Answer 98 : Mining Frequent Maximal Sequential Patterns using the VMSP Algorithm
• Question Answer 99 : Mining Frequent Sequential Generator Patterns Using the FEAT Algorithm
• Question Answer 100 : Mining Frequent Sequential Generator Patterns Using the FSGP Algorithm
• Question Answer 101 : Mining Frequent Sequential Generator Patterns Using the VGEN Algorithm
• Question Answer 102 : Mining Compressing Sequential Patterns Using the GoKrimp Algorithm
• Question Answer 103 : Mining Frequent Top-K Sequential Patterns Using the TKS Algorithm
• Question Answer 104 : Mining Frequent Top-K Sequential Patterns Using the TSP Algorithm
• Question Answer 105 : Mining Frequent Multi-dimensional Sequential Patterns Using SeqDIM (with PrefixSpan and Apriori)
• Question Answer 106 : Mining Frequent Closed Multi-dimensional Sequential Patterns Using SeqDIM/Songram (with Bide+ and AprioriClose)
• Question Answer 107 : Mining Sequential Patterns with Time Constraints from a Time-Extended Sequence Database
• Question Answer 108 : Mining Closed Sequential Patterns with Time Constraints from a Time-Extended Sequence Database
• Question Answer 109 : Mining Sequential Patterns with Time Constraints from a Time-Extended Sequence Database containing Valued Items (source code version only)
• Question Answer 110 : Mining Closed Multi-dimensional Sequential Patterns from a Time-Extended Sequence Database

Sequential Rule Mining

• Question Answer 111 : Mining Sequential Rules Common to Several Sequences with the CMRules algorithm
• Question Answer 112 : Mining Sequential Rules Common to Several Sequences with the CMDeo algorithm
• Question Answer 113 : Mining Sequential Rules Common to Several Sequences with the RuleGrowth algorithm
• Question Answer 114 : Mining Sequential Rules Common to Several Sequences with the ERMiner algorithm
• Question Answer 115 : Mining Sequential Rules between Sequential Patterns with the RuleGen algorithm
• Question Answer 116 : Mining Sequential Rules Common to Several Sequences with the Window Size Constraint using TRuleGrowth
• Question Answer 117 : Mining the Top-K Sequential rules
• Question Answer 118 : Mining the Top-K Non-Redundant Sequential rules

Sequence Prediction (source code version only)

• Question Answer 119 : Perform Sequence Prediction using the CPT+ Sequence Prediction Model
• Question Answer 120 : Perform Sequence Prediction using the CPT Sequence Prediction Model
• Question Answer 121 : Perform Sequence Prediction using the PPM Sequence Prediction Model
• Question Answer 122 : Perform Sequence Prediction using the DG Sequence Prediction Model
• Question Answer 123 : Perform Sequence Prediction using the AKOM Sequence Prediction Model
• Question Answer 124 : Perform Sequence Prediction using the TDAG Sequence Prediction Model
• Question Answer 125 : Perform Sequence Prediction using the LZ78 Sequence Prediction Model
• Question Answer 126 : Comparing Several Sequence Prediction Models

Periodic pattern mining

• Question Answer 127 : Mining Periodic Frequent Patterns using the PFPM algorithm
• Question Answer 128 : Mining Periodic High-Utility Itemsets using the PHM algorithm

Text Mining

• Question Answer 129 : Clustering Texts with a text clusterer
• Question Answer 130 : Classifying Text documents using a Naive Bayes approach (source code version only)

Time Series Mining

• Question Answer 131 : Vizualize time series using the time series viewer
• Question Answer 132 : Calculate the moving average of time series
• Question Answer 133 : Calculate the piecewise aggregate approximation of time series
• Question Answer 134 : Split time series by length
• Question Answer 135 : Split time series by number of segments
• Question Answer 136 : Convert time series to sequences using the SAX algorithm (useful to then apply sequential pattern mining/rule algorithms)
  
  Besides the above example for time series mining, clustering algorithms such as K-Means can also be applied to time-series.

Classification

• Question Answer 137 : Creating a decision tree with the ID3 algorithm to predict the value of a target attribute (source code version only)

Tools

• Question Answer 138 : Converting a sequence database to SPMF format (CSV, KOSARAK, IBM, BMS, Snake...)
• Question Answer 139 : Converting a transaction database to SPMF format (CSV...)
• Question Answer 140 : Converting a sequence database to a transaction database
• Question Answer 141 : Converting a transaction database to a sequence database
• Question Answer 142 : Generating a synthetic sequence database
• Question Answer 143 : Generating a synthetic sequence database with timestamps
• Question Answer 144 : Generating a synthetic transaction database
• Question Answer 145 : Generating synthetic utility values for a transaction database without utility values
• Question Answer 146 : Calculating statistics for a sequence database
• Question Answer 147 : Calculating statistics for a transaction database
• Question Answer 148 : Add consecutive timestamps to a sequence database without timestamps
• Question Answer 149 : Using the ARFF format in the source code version of SPMF
• Question Answer 150 : Using a TEXT file as input in the source code version of SPMF
• Question Answer 151 : Fix a transaction database
• Question Answer 152 : Fix item ids in a transaction database
• Question Answer 153 : Remove utility information from a transaction database
• Question Answer 154 : Resize a database in SPMF format (a text file)