Created using Colaboratory

UdayLab · Jul 22, 2023 · 80f5533 · 80f5533
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commit 80f5533
Showing 1 changed file with 14 additions and 15 deletions.
diff --git a/notebooks/CoMine.ipynb b/notebooks/CoMine.ipynb
@@ -4,8 +4,7 @@
   "metadata": {
     "colab": {
       "provenance": [],
-      "toc_visible": true,
-      "authorship_tag": "ABX9TyPngp8+oDXe9zaNSxQAgoty",
+      "authorship_tag": "ABX9TyP+p0/A83h+MyttQqcBVQjS",
       "include_colab_link": true
     },
     "kernelspec": {
@@ -30,7 +29,7 @@
     {
       "cell_type": "markdown",
       "source": [
-        "# Finding correlated patterns in a transactional database using CP-growth"
+        "# Finding correlated patterns in a transactional database using CoMine"
       ],
       "metadata": {
         "id": "Aj6UkFAj3sHh"
@@ -39,7 +38,7 @@
     {
       "cell_type": "markdown",
       "source": [
-        "This tutorial has three parts. In the first part, we describe the basic approach to find correlated patterns in a transactional database using the CP-growth algorithm. In the second part, we describe an advanced approach, where we evaluate the performance of CP-growth algorithm on a dataset at different *minimum support* values. In the final part, we describe an advanced approach, where we evaluate the performance of CP-growth algorithm on a dataset at different *minimum all-confidence* threshold values."
+        "This tutorial has three parts. In the first part, we describe the basic approach to find correlated patterns in a transactional database using the CoMine algorithm. In the second part, we describe an advanced approach, where we evaluate the performance of CoMine algorithm on a dataset at different *minimum support* values. In the final part, we describe an advanced approach, where we evaluate the performance of CoMine algorithm on a dataset at different *minimum all-confidence* threshold values."
       ],
       "metadata": {
         "id": "X-YPyS6G4AVR"
@@ -156,7 +155,7 @@
         "# This is formatted as code\n",
         "```\n",
         "\n",
-        "## Part 1: Finding correlated patterns using CP-growth"
+        "## Part 1: Finding correlated patterns using CoMine"
       ],
       "metadata": {
         "id": "pCnHao5L6PRR"
@@ -291,7 +290,7 @@
     {
       "cell_type": "markdown",
       "source": [
-        "### Step 4: Mining correlated patterns using CP-growth"
+        "### Step 4: Mining correlated patterns using CoMine"
       ],
       "metadata": {
         "id": "PlLmo9ZXDJkX"
@@ -300,9 +299,9 @@
     {
       "cell_type": "code",
       "source": [
-        "from PAMI.correlatedPattern.basic import CPGrowth  as alg #import the algorithm\n",
+        "from PAMI.correlatedPattern.basic import CoMine  as alg #import the algorithm\n",
         "\n",
-        "obj = alg.CPGrowth(iFile='Transactional_T10I4D100K.csv', minSup=minSup, minAllConf=minAllConf, sep='\\t')    #initialize\n",
+        "obj = alg.CoMine(iFile='Transactional_T10I4D100K.csv', minSup=minSup, minAllConf=minAllConf, sep='\\t')    #initialize\n",
         "obj.startMine()            #start the mining process\n",
         "\n",
         "obj.save('correlatedPatternsAtMinSupCount100_020.txt') #save the patterns\n",
@@ -365,7 +364,7 @@
     {
       "cell_type": "markdown",
       "source": [
-        "## Part 2: Evaluating the CP-growth algorithm on a dataset at different minSup values"
+        "## Part 2: Evaluating the CoMine algorithm on a dataset at different minSup values"
       ],
       "metadata": {
         "id": "FUO1nBPoHXJN"
@@ -384,7 +383,7 @@
       "cell_type": "code",
       "source": [
         "#Import the libraries\n",
-        "from PAMI.correlatedPattern.basic import CPGrowth  as alg #import the algorithm\n",
+        "from PAMI.correlatedPattern.basic import CoMine  as alg #import the algorithm\n",
         "import pandas as pd\n",
         "\n",
         "#Specify the input parameters\n",
@@ -455,10 +454,10 @@
       "cell_type": "code",
       "source": [
         "for minSup in minSupValues:\n",
-        "    obj = alg.CPGrowth(inputFile, minSup=minSup,minAllConf=minAllConf,sep=seperator)\n",
+        "    obj = alg.CoMine(inputFile, minSup=minSup,minAllConf=minAllConf,sep=seperator)\n",
         "    obj.startMine()\n",
         "    #store the results in the data frame\n",
-        "    result.loc[result.shape[0]] = ['CP-growth', minSup, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]"
+        "    result.loc[result.shape[0]] = ['CoMine', minSup, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]"
       ],
       "metadata": {
         "id": "9ivJHoSEJlky"
@@ -521,7 +520,7 @@
     {
       "cell_type": "markdown",
       "source": [
-        "## Part 3: Evaluating the CP-growth algorithm on a dataset at different minAllConf values"
+        "## Part 3: Evaluating the CoMine algorithm on a dataset at different minAllConf values"
       ],
       "metadata": {
         "id": "RO1D1sWIkn9J"
@@ -540,7 +539,7 @@
       "cell_type": "code",
       "source": [
         "#Import the libraries\n",
-        "from PAMI.correlatedPattern.basic import CPGrowth  as alg #import the algorithm\n",
+        "from PAMI.correlatedPattern.basic import CoMine  as alg #import the algorithm\n",
         "import pandas as pd\n",
         "\n",
         "#Specify the input parameters\n",
@@ -593,7 +592,7 @@
         "    obj = alg.CPGrowth(inputFile, minSup=minSupCount,minAllConf=minAllConf,sep=seperator)\n",
         "    obj.startMine()\n",
         "    #store the results in the data frame\n",
-        "    result.loc[result.shape[0]] = ['CP-growth', minAllConf, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]"
+        "    result.loc[result.shape[0]] = ['CoMine', minAllConf, len(obj.getPatterns()), obj.getRuntime(), obj.getMemoryRSS()]"
       ],
       "metadata": {
         "id": "HhoI6jP7lWsD"