{"id":915,"date":"2017-02-02T01:26:05","date_gmt":"2017-02-02T01:26:05","guid":{"rendered":"http:\/\/intelligentonlinetools.com\/blog\/?p=915"},"modified":"2017-02-05T14:16:53","modified_gmt":"2017-02-05T14:16:53","slug":"iris-data-set-normalized-data","status":"publish","type":"post","link":"http:\/\/intelligentonlinetools.com\/blog\/2017\/02\/02\/iris-data-set-normalized-data\/","title":{"rendered":"Iris Data Set – Normalized Data"},"content":{"rendered":"

On this page you can find normalized iris data set that was used in Iris Plant Classification Using Neural Network – Online Experiments with Normalization and Other Parameters<\/a>. The data set is divided to training data set (141 records) and testing data set (9 records, 3 for each class). Class label is shown separately.<\/p>\n

To calculate normalized data, the below table was built.<\/p>\n

min\t4.3\t2\t1\t0.1
\nmax\t7.9\t4.4\t6.9\t2.5
\nmax-min\t3.6\t2.4\t5.9\t2.4<\/p>\n

Here min, max and min-max are taken over the columns of iris data set which are:
\n 1. sepal length in cm
\n 2. sepal width in cm
\n 3. petal length in cm
\n 4. petal width in cm<\/p>\n

Training data set:<\/strong><\/p>\n

0.083333333\t0.458333333\t0.084745763\t0.041666667
\n0.194444444\t0.666666667\t0.06779661\t0.041666667
\n0.305555556\t0.791666667\t0.118644068\t0.125
\n0.083333333\t0.583333333\t0.06779661\t0.083333333
\n0.194444444\t0.583333333\t0.084745763\t0.041666667
\n0.027777778\t0.375\t0.06779661\t0.041666667
\n0.166666667\t0.458333333\t0.084745763\t0
\n0.305555556\t0.708333333\t0.084745763\t0.041666667
\n0.138888889\t0.583333333\t0.101694915\t0.041666667
\n0.138888889\t0.416666667\t0.06779661\t0
\n0\t0.416666667\t0.016949153\t0
\n0.416666667\t0.833333333\t0.033898305\t0.041666667
\n0.388888889\t1\t0.084745763\t0.125
\n0.305555556\t0.791666667\t0.050847458\t0.125
\n0.222222222\t0.625\t0.06779661\t0.083333333
\n0.388888889\t0.75\t0.118644068\t0.083333333
\n0.222222222\t0.75\t0.084745763\t0.083333333
\n0.305555556\t0.583333333\t0.118644068\t0.041666667
\n0.222222222\t0.708333333\t0.084745763\t0.125
\n0.083333333\t0.666666667\t0\t0.041666667
\n0.222222222\t0.541666667\t0.118644068\t0.166666667
\n0.138888889\t0.583333333\t0.152542373\t0.041666667
\n0.194444444\t0.416666667\t0.101694915\t0.041666667
\n0.194444444\t0.583333333\t0.101694915\t0.125
\n0.25\t0.625\t0.084745763\t0.041666667
\n0.25\t0.583333333\t0.06779661\t0.041666667
\n0.111111111\t0.5\t0.101694915\t0.041666667
\n0.138888889\t0.458333333\t0.101694915\t0.041666667
\n0.305555556\t0.583333333\t0.084745763\t0.125
\n0.25\t0.875\t0.084745763\t0
\n0.333333333\t0.916666667\t0.06779661\t0.041666667
\n0.166666667\t0.458333333\t0.084745763\t0
\n0.194444444\t0.5\t0.033898305\t0.041666667
\n0.333333333\t0.625\t0.050847458\t0.041666667
\n0.166666667\t0.458333333\t0.084745763\t0
\n0.027777778\t0.416666667\t0.050847458\t0.041666667
\n0.222222222\t0.583333333\t0.084745763\t0.041666667
\n0.194444444\t0.625\t0.050847458\t0.083333333
\n0.055555556\t0.125\t0.050847458\t0.083333333
\n0.027777778\t0.5\t0.050847458\t0.041666667
\n0.194444444\t0.625\t0.101694915\t0.208333333
\n0.222222222\t0.75\t0.152542373\t0.125
\n0.138888889\t0.416666667\t0.06779661\t0.083333333
\n0.222222222\t0.75\t0.101694915\t0.041666667
\n0.083333333\t0.5\t0.06779661\t0.041666667
\n0.277777778\t0.708333333\t0.084745763\t0.041666667
\n0.194444444\t0.541666667\t0.06779661\t0.041666667
\n0.333333333\t0.125\t0.508474576\t0.5
\n0.611111111\t0.333333333\t0.610169492\t0.583333333
\n0.388888889\t0.333333333\t0.593220339\t0.5
\n0.555555556\t0.541666667\t0.627118644\t0.625
\n0.166666667\t0.166666667\t0.389830508\t0.375
\n0.638888889\t0.375\t0.610169492\t0.5
\n0.25\t0.291666667\t0.491525424\t0.541666667
\n0.194444444\t0\t0.423728814\t0.375
\n0.444444444\t0.416666667\t0.542372881\t0.583333333
\n0.472222222\t0.083333333\t0.508474576\t0.375
\n0.5\t0.375\t0.627118644\t0.541666667
\n0.361111111\t0.375\t0.440677966\t0.5
\n0.666666667\t0.458333333\t0.576271186\t0.541666667
\n0.361111111\t0.416666667\t0.593220339\t0.583333333
\n0.416666667\t0.291666667\t0.525423729\t0.375
\n0.527777778\t0.083333333\t0.593220339\t0.583333333
\n0.361111111\t0.208333333\t0.491525424\t0.416666667
\n0.444444444\t0.5\t0.644067797\t0.708333333
\n0.5\t0.333333333\t0.508474576\t0.5
\n0.555555556\t0.208333333\t0.661016949\t0.583333333
\n0.5\t0.333333333\t0.627118644\t0.458333333
\n0.583333333\t0.375\t0.559322034\t0.5
\n0.638888889\t0.416666667\t0.576271186\t0.541666667
\n0.694444444\t0.333333333\t0.644067797\t0.541666667
\n0.666666667\t0.416666667\t0.677966102\t0.666666667
\n0.472222222\t0.375\t0.593220339\t0.583333333
\n0.388888889\t0.25\t0.423728814\t0.375
\n0.333333333\t0.166666667\t0.474576271\t0.416666667
\n0.333333333\t0.166666667\t0.457627119\t0.375
\n0.416666667\t0.291666667\t0.491525424\t0.458333333
\n0.472222222\t0.291666667\t0.694915254\t0.625
\n0.305555556\t0.416666667\t0.593220339\t0.583333333
\n0.472222222\t0.583333333\t0.593220339\t0.625
\n0.666666667\t0.458333333\t0.627118644\t0.583333333
\n0.555555556\t0.125\t0.576271186\t0.5
\n0.361111111\t0.416666667\t0.525423729\t0.5
\n0.333333333\t0.208333333\t0.508474576\t0.5
\n0.333333333\t0.25\t0.576271186\t0.458333333
\n0.5\t0.416666667\t0.610169492\t0.541666667
\n0.416666667\t0.25\t0.508474576\t0.458333333
\n0.194444444\t0.125\t0.389830508\t0.375
\n0.361111111\t0.291666667\t0.542372881\t0.5
\n0.388888889\t0.416666667\t0.542372881\t0.458333333
\n0.388888889\t0.375\t0.542372881\t0.5
\n0.527777778\t0.375\t0.559322034\t0.5
\n0.222222222\t0.208333333\t0.338983051\t0.416666667
\n0.388888889\t0.333333333\t0.525423729\t0.5
\n0.555555556\t0.541666667\t0.847457627\t1
\n0.416666667\t0.291666667\t0.694915254\t0.75
\n0.777777778\t0.416666667\t0.830508475\t0.833333333
\n0.555555556\t0.375\t0.779661017\t0.708333333
\n0.611111111\t0.416666667\t0.813559322\t0.875
\n0.916666667\t0.416666667\t0.949152542\t0.833333333
\n0.166666667\t0.208333333\t0.593220339\t0.666666667
\n0.833333333\t0.375\t0.898305085\t0.708333333
\n0.666666667\t0.208333333\t0.813559322\t0.708333333
\n0.805555556\t0.666666667\t0.86440678\t1
\n0.611111111\t0.5\t0.694915254\t0.791666667
\n0.583333333\t0.291666667\t0.728813559\t0.75
\n0.694444444\t0.416666667\t0.762711864\t0.833333333
\n0.388888889\t0.208333333\t0.677966102\t0.791666667
\n0.416666667\t0.333333333\t0.694915254\t0.958333333
\n0.583333333\t0.5\t0.728813559\t0.916666667
\n0.611111111\t0.416666667\t0.762711864\t0.708333333
\n0.944444444\t0.75\t0.966101695\t0.875
\n0.944444444\t0.25\t1\t0.916666667
\n0.472222222\t0.083333333\t0.677966102\t0.583333333
\n0.722222222\t0.5\t0.796610169\t0.916666667
\n0.361111111\t0.333333333\t0.661016949\t0.791666667
\n0.944444444\t0.333333333\t0.966101695\t0.791666667
\n0.555555556\t0.291666667\t0.661016949\t0.708333333
\n0.666666667\t0.541666667\t0.796610169\t0.833333333
\n0.805555556\t0.5\t0.847457627\t0.708333333
\n0.527777778\t0.333333333\t0.644067797\t0.708333333
\n0.5\t0.416666667\t0.661016949\t0.708333333
\n0.583333333\t0.333333333\t0.779661017\t0.833333333
\n0.805555556\t0.416666667\t0.813559322\t0.625
\n0.861111111\t0.333333333\t0.86440678\t0.75
\n1\t0.75\t0.915254237\t0.791666667
\n0.583333333\t0.333333333\t0.779661017\t0.875
\n0.555555556\t0.333333333\t0.694915254\t0.583333333
\n0.5\t0.25\t0.779661017\t0.541666667
\n0.944444444\t0.416666667\t0.86440678\t0.916666667
\n0.555555556\t0.583333333\t0.779661017\t0.958333333
\n0.583333333\t0.458333333\t0.762711864\t0.708333333
\n0.472222222\t0.416666667\t0.644067797\t0.708333333
\n0.722222222\t0.458333333\t0.745762712\t0.833333333
\n0.666666667\t0.458333333\t0.779661017\t0.958333333
\n0.722222222\t0.458333333\t0.694915254\t0.916666667
\n0.416666667\t0.291666667\t0.694915254\t0.75
\n0.694444444\t0.5\t0.830508475\t0.916666667
\n0.666666667\t0.541666667\t0.796610169\t1
\n0.666666667\t0.416666667\t0.711864407\t0.916666667
\n0.555555556\t0.208333333\t0.677966102\t0.75<\/p>\n

Testing data set<\/strong>
\n0.222222222\t0.625\t0.06779661\t0.041666667
\n0.166666667\t0.416666667\t0.06779661\t0.041666667
\n0.111111111\t0.5\t0.050847458\t0.041666667
\n0.75\t0.5\t0.627118644\t0.541666667
\n0.583333333\t0.5\t0.593220339\t0.583333333
\n0.722222222\t0.458333333\t0.661016949\t0.583333333
\n0.444444444\t0.416666667\t0.694915254\t0.708333333
\n0.611111111\t0.416666667\t0.711864407\t0.791666667
\n0.527777778\t0.583333333\t0.745762712\t0.916666667<\/p>\n

Training data set – class label values<\/strong><\/p>\n

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Testing data set – class label values<\/strong>
\n0
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On this page you can find normalized iris data set that was used in Iris Plant Classification Using Neural Network – Online Experiments with Normalization and Other Parameters. The data set is divided to training data set (141 records) and testing data set (9 records, 3 for each class). Class label is shown separately. To … Read more<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"jetpack_publicize_message":"","jetpack_is_tweetstorm":false,"jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":[]},"categories":[5,2,9],"tags":[],"jetpack_publicize_connections":[],"yoast_head":"\nIris Data Set - Normalized Data - Machine Learning Applications<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/intelligentonlinetools.com\/blog\/2017\/02\/02\/iris-data-set-normalized-data\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Iris Data Set - Normalized Data - Machine Learning Applications\" \/>\n<meta property=\"og:description\" content=\"On this page you can find normalized iris data set that was used in Iris Plant Classification Using Neural Network – Online Experiments with Normalization and Other Parameters. 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How differently will be results from running normalized and non normalized data? This will be explored in the post using Online Machine Learning Algorithms tool for classification of iris data set with feed-forward neural network. Feed-forward Neural Network Feed-forward neural\u2026","rel":"","context":"In "Artificial Intelligence"","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/intelligentonlinetools.com\/blog\/wp-content\/uploads\/2017\/01\/delta-error-graph-from-iris-data-set-36-neurons-300x231.png?resize=350%2C200","width":350,"height":200},"classes":[]},{"id":1289,"url":"http:\/\/intelligentonlinetools.com\/blog\/2017\/07\/03\/algorithms-metrics-and-online-tool-for-clustering\/","url_meta":{"origin":915,"position":1},"title":"Algorithms, Metrics and Online Tool for Clustering","date":"July 3, 2017","format":false,"excerpt":"One of the key techniques of exploratory data mining is clustering \u2013 separating instances into distinct groups based on some measure of similarity. [1] In this post we will review how we can do clustering, evaluate and visualize results using online ML Sandbox tool from this website. This tool allows\u2026","rel":"","context":"In "Data Mining"","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/intelligentonlinetools.com\/blog\/wp-content\/uploads\/2017\/07\/kmeans-clustering-iris-300x286.png?resize=350%2C200","width":350,"height":200},"classes":[]},{"id":1783,"url":"http:\/\/intelligentonlinetools.com\/blog\/2018\/01\/19\/machine-learning-stock-prediction-lstm-keras\/","url_meta":{"origin":915,"position":2},"title":"Machine Learning Stock Prediction with LSTM and Keras","date":"January 19, 2018","format":false,"excerpt":"In this post I will share experiments on machine learning stock prediction with LSTM and Keras with one step ahead. I tried to do first multiple steps ahead with few techniques described in the papers on the web. 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We\u2026","rel":"","context":"In "Data Mining"","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":450,"url":"http:\/\/intelligentonlinetools.com\/blog\/2016\/08\/03\/bio-inspired-optimization-for-text-mining-2\/","url_meta":{"origin":915,"position":4},"title":"Bio-Inspired Optimization for Text Mining-2","date":"August 3, 2016","format":false,"excerpt":"Numerical One Dimensional Example In the previous code Bio-Inspired Optimization for Text Mining-1 Motivation we implemented source code for optimization some function using bio-inspired algorithm. Now we need to put actual function for clustering. In clustering we want to group our clusters in such way that the distance from each\u2026","rel":"","context":"In "Data Mining"","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":1995,"url":"http:\/\/intelligentonlinetools.com\/blog\/2018\/04\/17\/lstm-neural-network-training-techniques-tuning-hyperparameters\/","url_meta":{"origin":915,"position":5},"title":"LSTM Neural Network Training – Few Useful Techniques for Tuning Hyperparameters and Saving Time","date":"April 17, 2018","format":false,"excerpt":"Neural networks are among the most widely used machine learning techniques.[1] But neural network training and tuning multiple hyper-parameters takes time. I was recently building LSTM neural network for prediction for this post Machine Learning Stock Market Prediction with LSTM Keras and I learned some tricks that can save time.\u2026","rel":"","context":"In "Machine Learning"","img":{"alt_text":"LSTM NN Training Value Loss Charts with High Number and Adjusted","src":"https:\/\/i0.wp.com\/intelligentonlinetools.com\/blog\/wp-content\/uploads\/2018\/04\/LSTM-NN-Training-Value-Loss-with-High-Number-and-adjusted-e1524097124417.png?resize=350%2C200","width":350,"height":200},"classes":[]}],"_links":{"self":[{"href":"http:\/\/intelligentonlinetools.com\/blog\/wp-json\/wp\/v2\/posts\/915"}],"collection":[{"href":"http:\/\/intelligentonlinetools.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/intelligentonlinetools.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/intelligentonlinetools.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/intelligentonlinetools.com\/blog\/wp-json\/wp\/v2\/comments?post=915"}],"version-history":[{"count":6,"href":"http:\/\/intelligentonlinetools.com\/blog\/wp-json\/wp\/v2\/posts\/915\/revisions"}],"predecessor-version":[{"id":959,"href":"http:\/\/intelligentonlinetools.com\/blog\/wp-json\/wp\/v2\/posts\/915\/revisions\/959"}],"wp:attachment":[{"href":"http:\/\/intelligentonlinetools.com\/blog\/wp-json\/wp\/v2\/media?parent=915"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/intelligentonlinetools.com\/blog\/wp-json\/wp\/v2\/categories?post=915"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/intelligentonlinetools.com\/blog\/wp-json\/wp\/v2\/tags?post=915"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}