Documentation

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

• Example : Mining Frequent Itemsets by Using the Apriori Algorithm
• Example 1 : Mining Frequent Itemsets by Using the AprioriTID Algorithm
• Example 2 : Mining Frequent Itemsets by Using the FP-Growth Algorithm
• Example 3 : Mining Frequent Itemsets by Using the Relim Algorithm
• Example 4 : Mining Frequent Itemsets by Using the Eclat / dEclat Algorithm
• Example 5 : Mining Frequent Itemsets by Using the H-Mine Algorithm
• Example 6 : Mining Frequent Itemsets by Using the FIN Algorithm
• Example 7 : Mining Frequent Itemsets by Using the DFIN Algorithm
• Example 8 : Mining Frequent Itemsets by Using the NegFIN Algorithm
• Example 9 : Mining Frequent Itemsets by Using the PrePost / PrePost+ Algorithm
• Example 10 : Mining Frequent Itemsets by Using the LCMFreq Algorithm
• Example 11 : Mining Frequent Closed Itemsets Using the AprioriClose Algorithm
• Example 12 : Mining Frequent Closed Itemsets Using the DCI_Closed Algorithm
• Example 13 : Mining Frequent Closed Itemsets Using the Charm / dCharm Algorithm
• Example 14 : Mining Frequent Closed Itemsets Using the LCM Algorithm
• Example 15 : Mining Frequent Closed Itemsets Using the FPClose Algorithm
• Example 16 : Mining Frequent Closed Itemsets Using the NAFCP Algorithm
• Example 17 : Mining Frequent Closed Itemsets Using the NEclatClosed Algorithm
• Example 18 : Mining Frequent Maximal Itemsets Using the FPMax Algorithm
• Example 19 : Mining Frequent Maximal Itemsets Using the Charm-MFI Algorithm
• Example 20 : Mining Frequent Generator Itemsets Using the DefMe Algorithm
• Example 21 : Mining Frequent Itemsets and Identify the Generators Using the Pascal Algorithm
• Example 22 : Mining Frequent Closed Itemsets and Minimal Generators Using the Zart Algorithm
• Example 23 : Mining Minimal Rare Itemsets Using the AprioriRare Algorithm
• Example 24 : Mining Perfectly Rare Itemsets Using the AprioriInverse Algorithm
• Example 25 : Mining Rare Correlated Itemsets Using the CORI Algorithm
• Example 26 : Mining Rare Itemsets Using the RP-Growth Algorithm
• Example 27 : Mining Closed Itemsets from a Data Stream Using the CloStream Algorithm (source code version only)
• Example 28 : Mining Recent Frequent Itemsets from a Data Stream Using the estDec Algorithm (source code version only)
• Example 29 : Mining Recent Frequent Itemsets from a Data Stream Using the estDec+ Algorithm (source code version only)
• Example 30 : Mining Frequent Itemsets from Uncertain Data with the UApriori Algorithm
• Example 31 : Mining Erasable Itemsets from a Product Database with the VME algorithm
• Example 32 : Building, updating incrementally and using an Itemset-Tree to generate targeted frequent itemsets and association rules (source code version only)
• Example 33 : Building, updating incrementally and using a Memory-Efficient Itemset-Tree to generate targeted frequent itemsets and association rules (source code version only)
• Example 34 : Mining Frequent Itemsets with Multiple Support Thresholds Using the MSApriori Algorithm
• Example 35 : Mining Frequent Itemsets with Multiple Support Thresholds Using the CFPGrowth++ Algorithm
• Example 36 : Mining Fuzzy Frequent Itemsets in a quantitative transaction database using the FFI-Miner algorithm
• Example 37 : Mining Multiple Fuzzy Frequent Itemsets in a quantitatve transaction database using the MFFI-Miner algorithm
• Example 38 : Deriving Frequent Itemsets from Frequent Closed Itemsets using the LevelWise algorithm
• Example 39 : Deriving Frequent Itemsets from Frequent Closed Itemsets using the DFI-Growth algorithm
• Example 40 : Deriving Frequent Itemsets from Frequent Closed Itemsets using the DFI-List algorithm
• Example 41 : Mining Self-Sufficient Itemsets using the Opus-Miner algorithm
• Example 42 : Mining the Top-K Frequent Itemsets by Using the Apriori(top-k) Algorithm
• Example 43 : Mining the Top-K Frequent Itemsets by Using the FP-Growth(top-k) Algorithm
• Example 44 : Mining Compressing Itemsets using the Krimp Algorithm
• Example 45 : Mining Compressing Itemsets using the SLIM Algorithm

High-Utility Pattern Mining

• Example 46 : Mining High-Utility Itemsets from a Transaction Database with Utility Information using the Two-Phase Algorithm
• Example 47 : Mining High-Utility Itemsets from a Transaction Database with Utility Information using the FHM Algorithm
• Example 48 : Mining High-Utility Itemsets from a Transaction Database with Utility Information using the EFIM Algorithm
• Example 49 : Mining High-Utility Itemsets from a Transaction Database with Utility Information using the HUI-Miner Algorithm
• Example 50 : Mining High-Utility Itemsets from a Transaction Database with Utility Information using the HUP-Miner Algorithm
• Example 51 : Mining High-Utility Itemsets from a Transaction Database with Utility Information using the UP-Growth / UP-Growth+ Algorithm
• Example 52 : Mining High-Utility Itemsets from a Transaction Database with Utility Information using the IHUP Algorithm
• Example 53 : Mining High-Utility Itemsets from a Transaction Database with Utility Information using the mHUIMiner Algorithm
• Example 54 : Mining High-Utility Itemsets from a Transaction Database with Utility Information using the HMiner Algorithm
• Example 55 : Mining High-Utility Itemsets from a Transaction Database with Utility Information using the HUIM-SU Algorithm
• Example 56 : Mining High-Utility Itemsets from a Transaction Database with Utility Information using the ULB-Miner Algorithm
• Example 57 : Mining High-Utility Itemsets from a Transaction Database with Utility Information using the UFH Algorithm
• Example 58 : Mining High-Utility Itemsets from a Transaction Database with Utility Information using the d2HUP Algorithm
• Example 59 : Mining High-Utility Itemsets from a Transaction Database with Utility Information using the FHIM Algorithm
• Example 60 : Mining High-Utility Itemsets from a Transaction Database with Utility Information using the UPHist Algorithm
• Example 61 : Mining High-Utility Itemsets from a Transaction Database with Utility Information while considering Length Constraints, using the FHM+ algorithm
• Example 62 : Mining Cross-Level High-Utility Itemsets from a Transaction Database with Utility Information using the CLH-Miner algorithm
• Example 63: Mining Cross-Level High-Utility Itemsets from a Transaction Database with Utility Information using the FEACP algorithm
• Example 64 : Mining the Multi-Level High-Utility Itemsets from a Transaction Database with Utility Information using the MLHUI-Miner algorithm
• Example 65 : Mining Correlated High-Utility Itemsets in a Transaction Database with Utility Information using the FCHM_bond algorithm
• Example 66 : Mining Correlated High-Utility Itemsets in a Transaction Database with Utility Information using the FCHM_allconfidence algorithm
• Example 67 : Mining Correlated High-Utility Itemsets in a Transaction Database with Utility Information using the ECHUM algorithm
• Example 68 : Mining Frequent High-Utility Itemsets from a Transaction Database with Utility Information using the FHMFreq Algorithm
• Example 69 : Mining High-Utility Itemsets from a Transaction Database with Positive or Negative Unit Profit using the FHN Algorithm
• Example 70 : Mining High-Utility Itemsets from a Transaction Database with Positive or Negative Unit Profit using the HUINIV-Mine Algorithm
• Example 71 : Mining On-Shelf High-Utility Itemsets from a Transaction Database using the FOSHU Algorithm
• Example 72 : Mining On-Shelf High-Utility Itemsets from a Transaction Database using the TS-HOUN Algorithm
• Example 73 : Incremental High-Utility Itemset Mining in a Transaction Database with utility information using the EIHI Algorithm (source code version only)
• Example 74 : Incremental High-Utility Itemset Mining in a Transaction Database with utility information using the HUI-LIST-INS Algorithm (source code version only)
• Example 75 : Incremental Closed High-Utility Itemset Mining in a Transaction Database with utility information using the INCCHUI Algorithm (source code version only)
• Example 76 : Mining Closed High-Utility Itemsets from a transaction database with utility information using the EFIM-Closed Algorithm
• Example 77 : Mining Closed High-Utility Itemsets from a transaction database with utility information using the CHUI-Miner Algorithm
• Example 78 : Mining Closed High-Utility Itemsets from a transaction database with utility information using the CHUI-Miner(Max) Algorithm
• Example 79 : Mining Closed High-Utility Itemsets from a transaction database with utility information using the CHUD Algorithm
• Example 80 : Mining Closed High-Utility Itemsets from a transaction database with utility information using the CLS-Miner Algorithm
• Example 81 : Mining Closed High-Utility Itemsets from a transaction database with utility information using the HMiner-Closed Algorithm
• Example 82 : Mining Generators of High-Utility Itemsets from a transaction database with utility information using the GHUI-Miner Algorithm
• Example 83 : Mining High-Utility Generator Itemsets from a transaction database with utility information using the HUG-Miner Algorithm
• Example 84: Mining Closed High Utility Itemsets and High-Utility Generator Itemsets from a transaction database with utility information using the HUCI_Miner Algorithm
• Example 85 : Mining Minimal High-Utility Itemsets from a transaction database with utility information using the MinFHM Algorithm
• Example 86 : Mining Skyline High-Utility Itemsets in a transaction database with utility information using the SkyMine Algorithm
• Example 87 : Mining Skyline Frequent High-Utility Itemsets in a transaction database with utility information using the SFUI_UF Algorithm
• Example 88 : Mining Skyline Frequent High-Utility Itemsets in a transaction database with utility information using the SFU-CE Algorithm
• Example 89 : Mining High-Utility Sequential Rules from a Sequence Database with utility information using the HUSRM Algorithm
• Example 90 : Mining High-Utility Sequential Patterns from a Sequence Database with utility information using the USPAN Algorithm
• Example 91 : Mining High-Utility Probability Sequential Patterns from a Sequence Database with utility and probability information using the PHUSPM Algorithm
• Example 92 : Mining High-Utility Probability Sequential Patterns from a Sequence Database with utility information and probability using the UHUSPM Algorithm
• Example 93 : Mining High-Utility Itemsets based on Particle Swarm Optimization with the HUIM-BPSO algorithm
• Example 94 : Mining High Utility Itemsets Using a Genetic Algorithm with the HUIM-GA algorithm
• Example 95 : Mining High Utility Itemsets Using Ant Colony Optimization Algorithm with the HUIM-ACO algorithm
• Example 96 : Mining High Utility Itemsets Using Hill-Climbing with the HUIM-HC algorithm
• Example 97 : Mining High Utility Itemsets Using Simulated Annealing with the HUIM-SA algorithm
• Example 98 : Mining High-Utility Itemsets based on Particle Swarm Optimization with the HUIM-BPSO-tree algorithm
• Example 99 : Discovery of High Utility Itemsets Using a Genetic Algorithm with the HUIM-GA-tree algorithm
• Example 100 : Mining High-Utility Itemsets based on Particle Swarm Optimization with the HUIF-PSO algorithm
• Example 101 : Mining High-Utility Itemsets based on Particle Swarm Optimization with the HUIM-SPSO algorithm
• Example 102 : Mining High-Utility Itemsets Using a Genetic Algorithm with the HUIF-GA algorithm
• Example 103 : Mining High-Utility Itemsets Using a Bat Algorithm with the HUIF-BA algorithm
• Example 104 : Mining High-Utility Itemsets Using Artificial Fish Swarm Algorithm with the HUIM-AF algorithm
• Example 105 : Mining High-Utility Itemsets Using a Artificial Bee Colony Algorithm with the HUIF-ABC algorithm
• Example 106 : Mining Skyline Frequent-Utility Patterns using the SFUPMinerUemax algorithm
• Example 107 : Mining Skyline Frequent-Utility Patterns using the EMSFUI_B algorithm
• Example 108 : Mining Skyline Frequent-Utility Patterns using the EMSFUI_D algorithm
• Example 109 : Mining the Top-k high-utility itemsets using the TKU algorithm
• Example 110 : Mining the Top-k high-utility itemsets using the TKO (basic) algorithm
• Example 111 : Mining the Top-k high-utility itemsets using the THUI algorithm
• Example 112 : Mining the Top-k high-utility itemsets using the TKU-CE algorithm
• Example 113 : Mining the Top-k high-utility itemsets using the TKU-CE+ algorithm
• Example 114 : Mining the Top-k high-utility itemsets in a data stream using the FHMDS algorithm
• Example 115 : Mining High Average-Utility Itemsets in a Transaction Database with Utility Information using the HAUI-Miner Algorithm
• Example 116 : Mining High Average-Utility Itemsets in a Transaction Database with Utility Information using the EHAUPM Algorithm
• Example 117 : Mining High Average-Utility Itemsets in a Transaction Database with Utility Information using the HAUIM-GMU Algorithm
• Example 118 : Mining High Average-Utility Itemsets with Multiple Thresholds in a Transaction Database using the HAUI-MMAU Algorithm
• Example 119 : Mining High Average-Utility Itemsets with Multiple Thresholds in a Transaction Database using the MEMU Algorithm
• Example 120 : Mining the Top-K High Average-Utility Itemsets in a Transaction Database using the ETAUIM Algorithm
• Example 121 : Mining Quantitative High Utility Itemsets in a Transaction Database using the VHUQI Algorithm
• Example 122 : Mining Quantitative High Utility Itemsets in a Transaction Database using the FHUQI-Miner Algorithm
• Example 123 : Mining the Top-K Quantitative High Utility Itemsets in a Transaction Database using the TKQ Algorithm
• Example 124 : Mining the Correlated Quantitative High Utility Itemsets in a Transaction Database using the CHUQI-Miner Algorithm
• Example 125 : Mining Irregular High-Utility Itemsets using the PHM_irregular algorithm
• Example 126 : Mining Local High Utility Itemsets in a Transaction Database using the LHUI-Miner Algorithm
• Example 127 : Mining Peak High Utility Itemsets in a Transaction Database using the PHUI-Miner Algorithm
• Example 128 : Mining Locally Trending High Utility Itemsets in a Transaction Database using the LTHUI-Miner Algorithm
• Example 129 : Mining Low-cost High Utility Itemsets using the LCIM algorithm
• Example 130 : Mining High Utility Association Rules using the HGB / HGB-ALL Algorithm

Association Rule Mining

• Example 131 : Mining All Association Rules
• Example 132 : Mining All Association Rules with the lift measure
• Example 133 : Mining All Association Rules using the GCD algorithm
• Example 134 : Mining the IGB basis of Association Rules
• Example 135 : Mining Perfectly Sporadic Association Rules
• Example 136 : Mining Closed Association Rules
• Example 137 : Mining Minimal Non Redundant Association Rules
• Example 138 : Mining Indirect Association Rules with the INDIRECT algorithm
• Example 139 : Hiding Sensitive Association Rules with the FHSAR algorithm.
• Example 140 : Mining the Top-K Association Rules
• Example 141 : Mining the Top-K Class Association Rules (association rules with a fixed consequent)
• Example 142 : Mining the Top-K Non-Redundant Association Rules

Clustering

• Example 143 : Clustering using the K-Means algorithm
• Example 144 : Clustering using the K-Means++ algorithm
• Example 145 : Clustering using the DBScan algorithm
• Example 146 : Using Optics to extract a cluster-ordering of points and DB-Scan style clusters
• Example 147 : Clustering using the Bisecting K-Means algorithm
• Example 148 : Clustering using a Hierarchical Clustering algorithm
• Example 149 : Generate a synthetic clustering dataset
• Example 150 : Visualizing clusters using the Cluster Viewer
• Example 151 : Visualizing instances using the Instance Viewer

Sequential Pattern Mining

• Example 152 : Mining Frequent Sequential Patterns Using the PrefixSpan Algorithm
• Example 153 : Mining Frequent Sequential Patterns Using the GSP Algorithm
• Example 154 : Mining Frequent Sequential Patterns Using the SPADE Algorithm
• Example 155 : Mining Frequent Sequential Patterns Using the CM-SPADE Algorithm
• Example 156 : Mining Frequent Sequential Patterns Using the SPAM Algorithm
• Example 157 : Mining Frequent Sequential Patterns Using the CM-SPAM Algorithm
• Example 158 : Mining Frequent Sequential Patterns Using the FAST Algorithm
• Example 159 : Mining Frequent Sequential Patterns Using the LAPIN Algorithm
• Example 160 : Mining Frequent Closed Sequential Patterns Using the ClaSP Algorithm
• Example 161 : Mining Frequent Closed Sequential Patterns Using the CM-ClaSP Algorithm
• Example 162 : Mining Frequent Closed Sequential Patterns Using the CloFAST Algorithm
• Example 163 : Mining Frequent Closed Sequential Patterns Using the CloSpan Algorithm
• Example 164 : Mining Frequent Closed Sequential Patterns Using the BIDE+ Algorithm
• Example 165 : Mining Frequent Closed Sequential Patterns by Post-Processing using SPAM or PrefixSpan
• Example 166 : Mining Frequent Maximal Sequential Patterns Using the MaxSP Algorithm
• Example 167 : Mining Frequent Maximal Sequential Patterns using the VMSP Algorithm
• Example 168 : Mining Frequent Sequential Generator Patterns Using the FEAT Algorithm
• Example 169 : Mining Frequent Sequential Generator Patterns Using the FSGP Algorithm
• Example 170 : Mining Frequent Sequential Generator Patterns Using the VGEN Algorithm
• Example 171: Mining Nonoverlapping Sequential Patterns in One or Many Sequences Using the NOSEP Algorithm
• Example 172 : Mining Compressing Sequential Patterns Using the GoKrimp Algorithm
• Example 173 : Mining Frequent Top-K Sequential Patterns Using the TKS Algorithm
• Example 174 : Mining Frequent Top-K Sequential Patterns Using the TSP Algorithm
• Example 175 : Mining Frequent Multi-dimensional Sequential Patterns Using SeqDIM (with PrefixSpan and Apriori)
• Example 176 : Mining Frequent Closed Multi-dimensional Sequential Patterns Using SeqDIM/Songram (with Bide+ and AprioriClose)
• Example 177 : Mining Sequential Patterns with Time Constraints from a Time-Extended Sequence Database
• Example 178 : Mining Sequential Patterns with flexible constraints from a Time-Extended Sequence Database with the SPM-FC-L algorithm
• Example 179 : Mining Sequential Patterns with flexible constraints from a Time-Extended Sequence Database with the SPM-FC-P algorithm
• Example 180 : Mining Closed Sequential Patterns with Time Constraints from a Time-Extended Sequence Database
• Example 181 : Mining Sequential Patterns with Time Constraints from a Time-Extended Sequence Database containing Valued Items (source code version only)
• Example 182 : Mining Closed Multi-dimensional Sequential Patterns from a Time-Extended Sequence Database
• Example 183 : Mining Progressive Sequential Patterns using the ProSecCo algorithm
• Example 184 : Finding all occurrences of some sequential pattern(s) by post-processing using the Occur algorithm
• Example 185 : the QCSP algorithm for mining the top-k quantive cohesive sequential patterns in a single sequence or in multiple sequences (thanks to Lens Fereman et al.)
• Example 186: Mining Cost-Efficient Sequential Patterns Using CEPB Algorithm
• Example 187: Mining Cost-Efficient Sequential Patterns Using CorCEPB Algorithm
• Example 188: Mining Cost-Efficient Sequential Patterns Using CEPN Algorithm
• Example 189: Mining Frequent Time Interval Related Patterns Using the FastTIRP Algorithm
• Example 190: Mining Frequent Time Interval Related Patterns Using the VertTIRP Algorithm

Sequential Rule Mining

• Example 191 : Mining Sequential Rules Common to Several Sequences with the CMRules algorithm
• Example 192 : Mining Sequential Rules Common to Several Sequences with the CMDeo algorithm
• Example 193 : Mining Sequential Rules Common to Several Sequences with the RuleGrowth algorithm
• Example 194 : Mining Sequential Rules Common to Several Sequences with the ERMiner algorithm
• Example 195 : Mining Sequential Rules between Sequential Patterns with the RuleGen algorithm
• Example 196 : Mining Sequential Rules Common to Several Sequences with the Window Size Constraint using TRuleGrowth
• Example 197 : Mining the Top-K Sequential rules
• Example 198: Mining the Top-K Class Sequential rules (sequential rules with a fixed consequent)
• Example 199 : Mining the Top-K Non-Redundant Sequential rules

Sequence Prediction (source code version only)

• Example 200 : Perform Sequence Prediction using the CPT+ Sequence Prediction Model
• Example 201 : Perform Sequence Prediction using the CPT Sequence Prediction Model
• Example 202 : Perform Sequence Prediction using the PPM Sequence Prediction Model
• Example 203 : Perform Sequence Prediction using the DG Sequence Prediction Model
• Example 204 : Perform Sequence Prediction using the AKOM Sequence Prediction Model
• Example 205 : Perform Sequence Prediction using the TDAG Sequence Prediction Model
• Example 206 : Perform Sequence Prediction using the LZ78 Sequence Prediction Model
• Example 207 : Comparing Several Sequence Prediction Models

Periodic pattern mining

• Example 208 : Mining Periodic Frequent Patterns using the PFPM algorithm
• Example 209 : Mining Local Periodic Frequent Patterns using the LPP-Growth, LPPM_breadth or LPPM_depth algorithm
• Example 210 : Mining Stable Periodic Frequent Patterns using the SPP-Growth algorithm
• Example 211 : Mining the Top-k Stable Periodic Frequent Patterns using the TSPIN algorithm
• Example 212 : Mining Self-Reliant Periodic Frequent Patterns using the SRPFPM algorithm
• Example 213 : Mining Productive Periodic Frequent Patterns using the PPFP algorithm
• Example 214 : Mining Non-Redundant Periodic Frequent Patterns using the NPFPM algorithm
• Example 215 : Mining Periodic High-Utility Itemsets using the PHM algorithm
• Example 216 : Mining Periodic High-Utility Itemsets with positive or negative utility values using the PHMN algorithm
• Example 217 : Mining Periodic High-Utility Itemsets with positive or negative utility values using the PHMN+ algorithm
• Example 218 : Mining Periodic Patterns in Multiple Sequences using the MPFPS-BFS or MPFPS-DFS algorithms
• Example 219 : Mining Rare Correlated Periodic Patterns in Multiple Sequences using the MRCPPS algorithm

Episode Mining

• Example 220 : Mining the Top-K Frequent Episodes in a Complex Sequence using the TKE algorithm
• Example 221 : Mining Frequent Episodes in a Complex Sequence using the EMMA algorithm, which counts the support based on the head frequency
• Example 222 : Mining Frequent Episodes in a Complex Sequence using the AFEM algorithm, which counts the support based on the head frequency
• Example 223 : Mining Frequent Episodes in a Complex Sequence using the MINEPI+ algorithm, which counts the support based on the head frequency
• Example 224 : Mining Frequent Episodes in a Complex Sequence using the MINEPI algorithm, which counts the support based on minimal occurrences
• Example 225 : Mining Maximal Frequent Episodes in a Complex Sequence using the MaxFEM algorithm, which counts the support based on the head frequency
• Example 226 : Mining Partially-Ordered Episode Rules in a Complex Sequence using the POERM, or POERM-ALL algorithms
• Example 227 : Mining Partially-Ordered Episode Rules in a Complex Sequence using the POERMH algorithm
• Example 228: Mining Episode Rules in a Complex Sequence using the TKE, MINEPI+, AFEM, or EMMA algorithm
• Example 229: Mining Episode Rules in a Complex Sequence with the Non-Overlapping Frequency using the NONEPI algorithm
• Example 230 : Mining High Utility Episodes using the HUE-SPAN algorithm
• Example 231 : Mining High Utility Episodes using the UP-SPAN algorithm
• Example 232 : Mining the Top-K High Utility Episodes using the TUP algorithm
• Example 233: Mining frequent sequential patterns with periodic wilcard gaps in a sequence of characters with the MAPD algorithm
• Example 234: Mining self-adaptive one-off weak-gap strong sequential patterns in a sequence of characters with the OWSP-Miner algorithm

Graph Pattern Mining

• Example 235 : Mining the Top-K Frequent Subgraphs in a Graph Database using the TKG algorithm
• Example 236 : Mining Frequent Subgraphs in a Graph Database using the gSpan algorithm
• Example 237 : Mining Frequent Closed Subgraphs in a Graph Database using the cgSpan algorithm
• Example 238 : Mining Significant Trend Sequence in a Dynamic Attributed Graph using the TSeqMiner algorithm

Text Mining

• Example 240 : Clustering Texts with a text clusterer
• Example 241 : Classifying Text documents using a Naive Bayes approach (source code version only)

Time Series Mining

• Example 242 : Vizualize time series using the time series viewer
• Example 243 : Calculate the prior moving average of time series
• Example 244 : Calculate the cumulative moving average of time series
• Example 245 : Calculate the central moving average of time series
• Example 246 : Calculate the min max normalization of a time series
• Example 247 : Calculate the standardization of a time series
• Example 248 : Calculate the median smoothing of a time series
• Example 249 : Calculate the exponential smoothing of a time series
• Example 250 : Calculate the first order differencing of a time series
• Example 251 : Calculate the second order differencing of a time series
• Example 252 : Calculate the piecewise aggregate approximation of time series
• Example 253 : Calculate the autocorelation function of a time series
• Example 254 : Calculate the regression line of a time series using the least square method, and perform time series forecasting
• Example 255 : Split time series by length
• Example 256 : Split time series by number of segments
• Example 257 : Convert time series to sequences using the SAX algorithm (useful to be able to apply sequential pattern mining/rule algorithms to time series)
  
  Besides the above example for time series mining, clustering algorithms such as K-Means can also be applied to time-series.

Classification

• Example 258 : How to train the ID3 classifier to perform classification (source code version only)
• Example 259 : How to train the KNN classifier to perform classification (source code version only)
• Example 260 : How to train the CMAR classifier to perform classification (source code version only)
• Example 261 : How to train the ACAC classifier to perform classification (source code version only)
• Example 262 : How to train the ACCF classifier to perform classification (source code version only)
• Example 263 : How to train the ACN classifier to perform classification (source code version only)
• Example 264 : How to train the ADT classifier to perform classification (source code version only)
• Example 265 : How to train the CBA classifier to perform classification (source code version only)
• Example 266 : How to train the CBA2 classifier to perform classification (source code version only)
• Example 267 : How to train the CMAR classifier to perform classification (source code version only)
• Example 268 : How to train the L3 classifier to perform classification (source code version only)
• Example 269 : How to train the CMAR classifier to perform classification (source code version only)
• Example 270 : How to train the MAC classifier to perform classification (source code version only)
• Example 271 : Run an experiment to compare many classifiers such as ID3, CMAR, ACCF, CBA and CBA2. (source code version only)

Dataset transformation tools

• Example 272 : Converting a sequence database to SPMF format (CSV, KOSARAK, IBM, BMS, Snake...)
• Example 273 : Converting a transaction database to SPMF format (CSV...)
• Example 274 : Converting a sequence database to a transaction database
• Example 275 : Converting a transaction database to a sequence database
• Example 276 : Converting a sequence database with cost values to a transaction database with cost values
• Example 277 : Generating a synthetic sequence database
• Example 278 : Generating a synthetic sequence database with timestamps
• Example 279 : Generating a synthetic transaction database
• Example 280 : Generating synthetic utility values for a transaction database without utility values
• Example 281 : Add consecutive timestamps to a sequence database without timestamps
• Example 282 : Using the ARFF format in the source code version of SPMF
• Example 283 : Using a TEXT file as input in the source code version of SPMF
• Example 284 : Fix a transaction database
• Example 285 : Fix a sequence database
• Example 286 : Fix item ids in a transaction database
• Example 287 : Fix item ids in a transaction database with utility information
• Example 288 : Fix a transaction database with utility and time information
• Example 289 : Remove utility information from a transaction database
• Example 290 : Resize a database in SPMF format (a text file)

Dataset statistics tools

• Example 291 : Calculating statistics for a sequence database
• Example 292 : Calculating statistics for a transaction database
• Example 293 : Calculating statistics for a transaction database with utility information
• Example 294 : Calculating statistics for a graph database
• Example 295 : Calculating statistics for a product transaction database
• Example 296 : Calculating statistics for a sequence database with cost and binary utility
• Example 297 : Calculating statistics for a sequence database with cost and numeric utility
• Example 298 : Calculating statistics for a sequence database with utility
• Example 299 : Calculating statistics for a time-extended sequence database
• Example 300 : Calculating statistics for an uncertain transaction database
• Example 301 : Calculating statistics for a transaction database with cost and utility
• Example 302 : Calculating statistics for a transaction database with utility and period information
• Example 303 : Calculating statistics for a transaction database with utility and timestamps
• Example 304 : Calculating statistics for an event sequence
• Example 305 : Calculating statistics for an interval sequence database
• Example 306 : Calculating statistics for a multi-dimensional sequence database
• Example 307 : Calculating statistics for a multi-dimensional sequence database with timestamps
• Example 308 : Calculating statistics for a file with double vectors (instances) for clustering
• Example 309 : Calculating statistics for time series

Dataset viewer tools

• Example 310 : View the content of an ARFF file using the ARFF Viewer
• Example 311 : View a graph database file with the Graph Viewer
• Example 312 : View an event sequence with the Event Sequence Viewer
• Example 313 : View a sequence database file with the Sequence Database Viewer
• Example 314 : View a sequence database cost binary utility file with the Sequence Database Cost Binary Utility Viewer
• Example 315 : View a sequence database cost numeric utility file with the Sequence Database Cost Numeric Utility Viewer
• Example 316 : View a time-extended sequence database with the Time-Extended Sequence Database Viewer
• Example 317 : View an MD sequence database with the MD Sequence Database Viewer
• Example 318 : View an MD time sequence database with the MD Time Sequence Database Viewer
• Example 319 : View a sequence utility database file with the Sequence Utility Database Viewer
• Example 320 : View a transaction database file with the Transaction Database Viewer
• Example 321 : View an uncertain transaction database file with the Uncertain Transaction Database Viewer
• Example 322 : View a utility transaction database file with the Utility Transaction Database Viewer
• Example 323 : View a utility time transaction database file with the Utility Time Transaction Database Viewer
• Example 324 : View a utility period transaction database file with the Utility Period Transaction Database Viewer
• Example 325 : View a product transaction database file with the Profit Transaction Database Viewer
• Example 326 : View a cost utility transaction database file with the Cost Utility Transaction Database Viewer
• Example 327 : View a Time-Interval Sequence Database with the Time Interval Sequence Database Viewer

Other GUI tools

• Example 328 : Open the Algorithm Explorer to view informations about algorithms
• Example 329 : Create a workflow using the SPMF Workflow editor
• Example 330 : Monitor the JVM's Memory using the Memory Viewer
• Example 331 : Visualize patterns in a text file with the Pattern Viewer
• Example 332 : Open a text file using the SPMF text editor
• Example 333 : Open a text file with the System Text Editor
• Example 334 : Open the SPMF developers tools
• Example 335 : Download an offline copy of the SPMF documentation on your computer

Experiments

• Example 336 : Run experiments to compare the performance of one or more algorithms on a dataset (one parameter is varied)

Copyright © 2008-2024 Philippe Fournier-Viger. All rights reserved.