{"id":46805,"date":"2024-07-04T00:00:00","date_gmt":"2024-07-04T07:00:00","guid":{"rendered":"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/blog\/normalization\/"},"modified":"2025-11-13T12:57:02","modified_gmt":"2025-11-13T20:57:02","slug":"normalization","status":"publish","type":"post","link":"https:\/\/www.griddb.net\/en\/blog\/normalization\/","title":{"rendered":"Normalization in Java"},"content":{"rendered":"

In this blog post, we will demonstrate normalizing data for machine learning with Java and GridDB.<\/p>\n

Source code for this blog can be found on our Github page: https:\/\/github.com\/griddbnet\/Blogs<\/p>\n

\n
$ git clone https:\/\/github.com\/griddbnet\/Blogs.git --branch normalization<\/code><\/pre>\n<\/div>\n
Before we get started, Let’s understand normalization first.<\/p>\n
What is Normalization?<\/h2>\n
Normalization is a commonly used data preparation technique in Machine Learning that changes the values of columns in the dataset using a common scale. Normalization is required when a range of characteristics are present in the dataset.<\/p>\n
In our project, we’re going to use the dataset house.csv<\/code> that looks something like this:<\/p>\n
\n
\nhousesize,lotsize,bedrooms,granite,bathroom,sellingprice\n3529,9191,6,0,0,205000 \n3247,10061,5,1,1,224900 \n4032,10150,5,0,1,197900 \n2397,14156,4,1,0,189900\n2200,9600,4,0,1,195000\n3536,19994,6,1,1,325000\n2983,9365,5,0,1,230000<\/code><\/pre>\n<\/div>\n
You can see that the dataset contains large numbers with different ranges, making it difficult to perform data analytics operations.<\/p>\n
With normalization, we can simplify this dataset by changing the column values on a common scale, such as ranging column values between 0 and 1. Looks something like this.<\/p>\n
\n
\nhousesize,lotsize,bedrooms,granite,bathroom,sellingprice\n0.571507,0.080533,0.5,1,1,224900\n0.107533,0.459595,0,1,0,189900\n0.729258,1,1,1,1,325000\n0.725437,0,1,0,0,205000\n1,0.088772,0.5,0,1,197900\n0,0.03786,0,0,1,195000\n0.427402,0.016107,0.5,0,1,230000<\/code><\/pre>\n<\/div>\n
You may notice that the column sellingprice<\/code> remains unchanged. This is because the classifier must know which is our outcome variable.<\/p>\n
That’s why we set the column sellingprice<\/code> as our class index for the dataset before passing it to the classifier. You will observe it practically later in this article.<\/p>\n
The following formula defines normalization mathematically.<\/p>\n
Xn = (X – Xmin) \/ (Xmax – Xmin)<\/p>\n
Where,
\nXn = Normalized value.
\nXmin = Minimum value of a feature.
\nXmax = Maximum value of a feature.<\/p>\n
Types of Normalization<\/h2>\n
Many types of normalization are available in machine learning. But the following types are widely used:<\/p>\n
Min-Max Scaling:<\/strong> This type of normalization subtracts the minimum value from the highest value of each column and divides it by the range. Each new column has a minimum value of 0 and a maximum value of 1.<\/p>\n
Standardization Scaling:<\/strong> Also known as Z-score normalization centers a variable at zero and standardizes the variance at one. It first subtracts the mean of each observation and then divides it by the standard deviation.<\/p>\n
That’s enough for the theory. Let’s dive into something practical.<\/p>\n
How to use Normalization?<\/h2>\n
Project Setup<\/h2>\n
Our project implements a basic level of normalization in Java and GridDB. Here is the project organization.<\/p>\n
To run this project, your system should have:
\n– Java (Latest version)
\n– GridDB<\/p>\n
Required JAR files<\/h3>\n
We need two JAR files gridstore-5.5.0.jar<\/code> and weka-3.7.0.jar<\/code>. Here, gridstore-5.5.0.jar<\/code> is an official library for GridDB, and weka-3.7.0.jar<\/code> is a Machine Learning library used for data analysis.<\/p>\n
Download the Weka JAR from the following URL:<\/p>\n
http:\/\/www.java2s.com\/Code\/Jar\/w\/weka.htm<\/a><\/p>\n
Download the gridstore.jar from here:<\/p>\n
https:\/\/mvnrepository.com\/artifact\/com.github.griddb\/gridstore-jdbc\/5.5.0<\/a><\/p>\n
Import Packages<\/h3>\n
For this project, we need some basic Java packages, such as:<\/p>\n
\n
\nimport java.util.Properties;\nimport java.util.Scanner;\nimport java.io.File;\nimport java.io.Reader;\nimport java.io.StringReader;<\/code><\/pre>\n<\/div>\n
Include the following packages to connect and operate GridDB:<\/p>\n
\n
\nimport com.toshiba.mwcloud.gs.Collection;\nimport com.toshiba.mwcloud.gs.GSException;\nimport com.toshiba.mwcloud.gs.GridStore;\nimport com.toshiba.mwcloud.gs.GridStoreFactory;\nimport com.toshiba.mwcloud.gs.Query;\nimport com.toshiba.mwcloud.gs.RowKey;\nimport com.toshiba.mwcloud.gs.RowSet;<\/code><\/pre>\n<\/div>\n
For data analysis, include these packages from Weka:<\/p>\n
\n
\nimport weka.classifiers.Evaluation;\nimport weka.classifiers.functions.LinearRegression;\nimport weka.core.Instances;\nimport weka.filters.Filter;\nimport weka.filters.unsupervised.attribute.Normalize;<\/code><\/pre>\n<\/div>\n
Well, that’s the initial step.<\/p>\n
Let’s move on to our next step.<\/p>\n
Creating a Class for Storing Data<\/h2>\n
Our project stores data in a GridDB container. So, we need to define the container schema as a static class:<\/p>\n
\n
\nstatic class House {\n    @RowKey int housesize;\n    int lotsize;\n    int bedrooms;\n    int granite;\n    int bathroom;\n    int sellingprice;\n}<\/code><\/pre>\n<\/div>\n
The static class House<\/code> consists of six integer-type columns with a RowKey<\/code> housesize<\/code>.<\/p>\n
Creating a GridDB Connection<\/h2>\n
We have the schema class ready. It’s time to connect our program with GridDB.<\/p>\n
For creating GridDB connection, we have to initiate a Properties<\/code> instance defining the credentials for GridDB installation. Have a look at the following code:<\/p>\n
\n
\nProperties props = new Properties();\nprops.setProperty(\"notificationMember\", \"127.0.0.1:10001\");\nprops.setProperty(\"clusterName\", \"myCluster\");\nprops.setProperty(\"user\", \"admin\");\nprops.setProperty(\"password\", \"admin\");\nGridStore store = GridStoreFactory.getInstance().getGridStore(props);<\/code><\/pre>\n<\/div>\n
You may need to change these connection specifics based on your GridDB installation.<\/p>\n
Once you have successfully created your connection, it’s time to create a collection.<\/p>\n
\n
\nCollection coll = store.putCollection(\"House\", House.class);<\/code><\/pre>\n<\/div>\n
Here, we created a collection object coll<\/code> for the container House<\/code>.<\/p>\n
Push Data to GridDB<\/h2>\n
Now we have our GridDB schema and our connection is ready to go.<\/p>\n
Let’s put some data to it,<\/p>\n
In this purpose, we will retrive data from the house.csv<\/code> file and push them to GridDB. The following code block achieves this task,<\/p>\n
\n
\nFile file1 = new File(\"house.csv\");\nScanner scan = new Scanner(file1);\nString data = scan.next();\n\nwhile (scan.hasNext()){\n    String scanData = scan.next();\n    String dataList[] = scanData.split(\",\");\n    String housesize = dataList[0];\n    String lotsize = dataList[1];\n    String bedrooms = dataList[2];\n    String granite = dataList[3];\n    String bathroom = dataList[4];\n    String sellingprice = dataList[5];\n    \n    House hs = new House();\n    \n    hs.housesize = Integer.parseInt(housesize);\n    hs.lotsize = Integer.parseInt(lotsize);\n    hs.bedrooms = Integer.parseInt(bedrooms);\n    hs.granite = Integer.parseInt(granite);\n    hs.bathroom = Integer.parseInt(bathroom);\n    hs.sellingprice = Integer.parseInt(sellingprice);\n    coll.put(hs);\n}<\/code><\/pre>\n<\/div>\n
We created an object of our schema class House<\/code> to push data to the container.<\/p>\n
Retrieve Data from GridDB<\/h2>\n
Let’s pull data from the GridDB container and make the data available for analysis. Use the following code.<\/p>\n
\n
\nQuery query = coll.query(\"select *\");\nRowSet rs = query.fetch(false);<\/code><\/pre>\n<\/div>\n
Query data from GridDB is similar to general SQL queries. Here, we used the query select *<\/code> to retrieve all the data from the GridDB container.<\/p>\n
We’ll save these data into a string for our next step.<\/p>\n
\n
\nString datastr = \"@RELATION housenn@ATTRIBUTE houseSize NUMERICn@ATTRIBUTE lotSize NUMERICn@ATTRIBUTE bedrooms NUMERICn@ATTRIBUTE granite NUMERICn@ATTRIBUTE bathroom NUMERICn@ATTRIBUTE sellingPrice NUMERICnn@DATAn\";\n\nwhile (rs.hasNext()) {\nHouse hs = rs.next();\ndatastr = datastr + hs.housesize+\",\"+hs.lotsize+\",\"+hs.bedrooms+\",\"+hs.granite+\",\"+hs.bathroom+\",\"+hs.sellingprice+\"n\";\n}<\/code><\/pre>\n<\/div>\n
Notice that we initialize the string datastr<\/code> with the arff<\/code> file structure.<\/p>\n
Let’s see what the data looks like before normalization,<\/p>\n
\n
\nSystem.out.println(\"==== Before Normalization ===n\"+datastr);<\/code><\/pre>\n<\/div>\n
Creating Weka Instances<\/h2>\n
We will use the Weka Machine Learning library for normalizing data. Hence, we need to convert our data into Weka Instances.<\/p>\n
Do you remember we stored data in the string datastr<\/code> in our previous step?<\/p>\n
Yes, we’re going to pass that string in our Weka instances.<\/p>\n
\n
\nReader dataString = new StringReader(datastr);\nInstances dataset = new Instances(dataString);\ndataset.setClassIndex(dataset.numAttributes()-1);<\/code><\/pre>\n<\/div>\n
The above code creates a Weka Instance.<\/p>\n
Normalization<\/h2>\n
Let’s perform normalization to our dataset,<\/p>\n
\n
\nNormalize normalize = new Normalize();\nnormalize.setInputFormat(dataset);\nInstances newdata = Filter.useFilter(dataset, normalize);<\/code><\/pre>\n<\/div>\n
Next, we are going to visualize data after normalization<\/p>\n
\n
\nSystem.out.println(\"==== After Normalization ===n\"+newdata);<\/code><\/pre>\n<\/div>\n
Lastly, we just applied linear regression to our normalized data.<\/p>\n
\n
\nLinearRegression lr = new LinearRegression();\nlr.buildClassifier(newdata);\nEvaluation lreval = new Evaluation(newdata);\nlreval.evaluateModel(lr, newdata);\nSystem.out.println(lreval.toSummaryString());<\/code><\/pre>\n<\/div>\n
That’s all about the project. Let’s test it.<\/p>\n
Compile and Execute<\/h2>\n
If you aren’t using any IDE, you may need to include the JAR files through the classpath:<\/p>\n
\n
\nexport CLASSPATH=$CLASSPATH:\/Home\/User\/Download\/gridstore.jar<\/code><\/pre>\n<\/div>\n
\n
\nexport CLASSPATH=$CLASSPATH:\/Home\/User\/Download\/weka-3.7.0.jar<\/code><\/pre>\n<\/div>\n
These paths may differ based on your setup.<\/p>\n
Now, you can compile the code using this command:<\/p>\n
\n
\njavac NormalizationJava.java<\/code><\/pre>\n<\/div>\n
Then execute the code:<\/p>\n
\n
\njava NormalizationJava<\/code><\/pre>\n<\/div>\n
Showing the Output<\/h2>\n
Running the code, you’ll have the following output:<\/p>\n
\n
\n==== Before Normalization ===\n@RELATION house\n\n@ATTRIBUTE houseSize NUMERIC\n@ATTRIBUTE lotSize NUMERIC\n@ATTRIBUTE bedrooms NUMERIC\n@ATTRIBUTE granite NUMERIC\n@ATTRIBUTE bathroom NUMERIC\n@ATTRIBUTE sellingPrice NUMERIC\n\n@DATA\n3247,10061,5,1,1,224900\n2397,14156,4,1,0,189900\n3536,19994,6,1,1,325000\n3529,9191,6,0,0,205000\n4032,10150,5,0,1,197900\n2200,9600,4,0,1,195000\n2983,9365,5,0,1,230000\n\n==== After Normalization ===\n@relation house-weka.filters.unsupervised.attribute.Normalize-S1.0-T0.0\n\n@attribute houseSize numeric\n@attribute lotSize numeric\n@attribute bedrooms numeric\n@attribute granite numeric\n@attribute bathroom numeric\n@attribute sellingPrice numeric\n\n@data\n0.571507,0.080533,0.5,1,1,224900\n0.107533,0.459595,0,1,0,189900\n0.729258,1,1,1,1,325000\n0.725437,0,1,0,0,205000\n1,0.088772,0.5,0,1,197900\n0,0.03786,0,0,1,195000\n0.427402,0.016107,0.5,0,1,230000\n\nCorrelation coefficient                  0.9945\nMean absolute error                   4053.821 \nRoot mean squared error               4578.4125\nRelative absolute error                 13.1339 %\nRoot relative squared error             10.51   %\nTotal Number of Instances                7     \n\nBUILD SUCCESSFUL (total time: 1 second)<\/code><\/pre>\n<\/div>\n
Wrapping Up<\/h2>\n
That’s all for normalization in Java with GridDB.<\/p>\n
This article demonstrates applying normalization to the dataset for better analysis. As we mentioned, normalization is helpful when the dataset contains a range of characteristics.<\/p>\n
Finally, make sure you close the query, container, and the GridDB database.<\/p>\n
\n
\nquery.close();\ncoll.close();\nstore.close();<\/code><\/pre>\n<\/div>\n","protected":false},"excerpt":{"rendered":"
In this blog post, we will demonstrate normalizing data for machine learning with Java and GridDB. Source code for this blog can be found on our Github page: https:\/\/github.com\/griddbnet\/Blogs $ git clone https:\/\/github.com\/griddbnet\/Blogs.git –branch normalization Before we get started, Let’s understand normalization first. What is Normalization? Normalization is a commonly used data preparation technique in […]<\/p>\n","protected":false},"author":41,"featured_media":30155,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[121],"tags":[],"class_list":["post-46805","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog"],"acf":[],"yoast_head":"\nNormalization in Java | GridDB: Open Source Time Series Database for IoT<\/title>\n<meta name=\"description\" content=\"In this blog post, we will demonstrate normalizing data for machine learning with Java and GridDB. Source code for this blog can be found on our Github\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.griddb.net\/en\/blog\/normalization\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Normalization in Java | GridDB: Open Source Time Series Database for IoT\" \/>\n<meta property=\"og:description\" content=\"In this blog post, we will demonstrate normalizing data for machine learning with Java and GridDB. 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