This article will cover the creation of wine clusters based on a Wine dataset's different attributes. R Programming will be used, which is very useful in creating a set of groups representing some of the differences and similarities found in the types of wines. In addition, GridDB will be our central database for this program as it is ideally suited to hold machine learning datasets. The article will outline the requirements to set up our database GridDB. Following that, we will briefly describe our dataset and model. To finish off, we will interpret the results and come up with our conclusion.
Prerequisites¶
sessionInfo()
R version 4.2.2 Patched (2022-11-10 r83330) Platform: x86_64-pc-linux-gnu (64-bit) Running under: Ubuntu 20.04.5 LTS Matrix products: default BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/liblapack.so.3 locale: [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 [7] LC_PAPER=en_US.UTF-8 LC_NAME=C [9] LC_ADDRESS=C LC_TELEPHONE=C [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: [1] factoextra_1.0.7 RJDBC_0.2-10 rJava_1.0-6 DBI_1.1.3 [5] forcats_1.0.0 stringr_1.4.1 dplyr_1.1.0 purrr_1.0.1 [9] readr_2.1.3 tidyr_1.3.0 tibble_3.1.8 ggplot2_3.4.0 [13] tidyverse_1.3.1 loaded via a namespace (and not attached): [1] ggrepel_0.9.3 Rcpp_1.0.10 lubridate_1.9.1 assertthat_0.2.1 [5] digest_0.6.30 utf8_1.2.3 IRdisplay_1.1 R6_2.5.1 [9] cellranger_1.1.0 repr_1.1.6 backports_1.4.1 reprex_2.0.2 [13] evaluate_0.20 httr_1.4.4 pillar_1.8.1 rlang_1.0.6 [17] uuid_1.1-0 readxl_1.4.1 rstudioapi_0.14 munsell_0.5.0 [21] broom_1.0.3 compiler_4.2.2 modelr_0.1.10 pkgconfig_2.0.3 [25] base64enc_0.1-3 htmltools_0.5.4 tidyselect_1.2.0 fansi_1.0.4 [29] crayon_1.5.2 tzdb_0.3.0 dbplyr_2.3.0 withr_2.5.0 [33] grid_4.2.2 jsonlite_1.8.3 gtable_0.3.1 lifecycle_1.0.3 [37] magrittr_2.0.3 scales_1.2.1 cli_3.6.0 stringi_1.7.12 [41] fs_1.6.0 xml2_1.3.3 ellipsis_0.3.2 generics_0.1.3 [45] vctrs_0.5.2 IRkernel_1.3.2 tools_4.2.2 glue_1.6.2 [49] hms_1.1.2 fastmap_1.1.0 timechange_0.2.0 colorspace_2.1-0 [53] rvest_1.0.3 pbdZMQ_0.3-9 haven_2.5.1
Requirements¶
The GridDB database storage system will store our dataset while building our clustering machine learning model. GridDB must be downloaded and configured in your operating system to be fully functional.
Make sure to run the following R statements to import the needed libraries helpful in running our Wine Cluster:
install.packages( "RJDBC")
install.packages("factoextra")
Installing package into ‘/usr/local/lib/R/site-library’ (as ‘lib’ is unspecified) Installing package into ‘/usr/local/lib/R/site-library’ (as ‘lib’ is unspecified)
library(tidyverse)
library(RJDBC)
library(factoextra)
Setup GridDB¶
To set up the GridDB database, we will initialize the connection object by specifying the driverClass and classPath attributes.
The following task can be as follows:
drv <- JDBC("com.toshiba.mwcloud.gs.sql.Driver",
"/usr/share/java/gridstore-jdbc-5.0.0.jar")
#identifier.quote = "`")
The second step is to set up the IP address and port with the credentials in mind. These values will depend on your configuration and credentials.
The task can be done as follows:
conn <- dbConnect(drv, "jdbc:gs://127.0.0.1:20001/myCluster/public", "admin", "admin")
Store Data in GridDB¶
In this section, we will store the data in our GridDB database. We will use the CREATE query to complete this task, creating a table representing your wine dataset.
The following code was used to conduct the task explained in this section:
dbInsertTable <- function(conn, name,
df, append = TRUE){
for (i in seq_len(nrow(df))) {
dbWriteTable(conn, name, df[i, ], append = append)
}
}
One additional step is to use the function created to add the table to our GridDB database:
Alcohol <dbl> 14.23, 13.20, 13.16, 14.37, 13.24, 14.20, 14.39, …
$ Malic_Acid <dbl> 1.71, 1.78, 2.36, 1.95, 2.59, 1.76, 1.87, 2.15, 1…
$ Ash <dbl> 2.43, 2.14, 2.67, 2.50, 2.87, 2.45, 2.45, 2.61, 2…
$ Ash_Alcanity <dbl> 15.6, 11.2, 18.6, 16.8, 21.0, 15.2, 14.6, 17.6, 1…
$ Magnesium <dbl> 127, 100, 101, 113, 118, 112, 96, 121, 97, 98, 10…
$ Total_Phenols <dbl> 2.80, 2.65, 2.80, 3.85, 2.80, 3.27, 2.50, 2.60, 2…
$ Flavanoids <dbl> 3.06, 2.76, 3.24, 3.49, 2.69, 3.39, 2.52, 2.51, 2…
$ Nonflavanoid_Phenols <dbl> 0.28, 0.26, 0.30, 0.24, 0.39, 0.34, 0.30, 0.31, 0…
$ Proanthocyanins <dbl> 2.29, 1.28, 2.81, 2.18, 1.82, 1.97, 1.98, 1.25, 1…
$ Color_Intensity <dbl> 5.64, 4.38, 5.68, 7.80, 4.32, 6.75, 5.25, 5.05, 5…
$ Hue <dbl> 1.04, 1.05, 1.03, 0.86, 1.04, 1.05, 1.02, 1.06, 1…
$ OD280 <dbl> 3.92, 3.40, 3.17, 3.45, 2.93, 2.85, 3.58, 3.58, 2…
$ Proline <dbl> 1065, 1050, 1185, 1480, 735, 1450, 1290, 1295, 10…
$ Customer_Segment
dbSendUpdate(conn, paste(
"CREATE TABLE IF NOT EXISTS wine_clusters",
"( Alcohol FLOAT,
Malic_Acid FLOAT,
Ash FLOAT,
Ash_Alcanity FLOAT,
Magnesium FLOAT,
Total_Phenols FLOAT,
Flavanoids FLOAT,
Nonflavanoid_Phenols FLOAT,
Proanthocyanins FLOAT,
Color_Intensity FLOAT,
Hue FLOAT,
OD280 FLOAT,
Proline FLOAT,
Customer_Segment FLOAT);"))
To implement the clustering algorithm, we will use the wine dataset to create wine clusters. The dataset contains 14 attributes and 178 instances. The original dataset is provided by UCI Machine Learning Repositor in the following link: http://archive.ics.uci.edu/ml/datasets/Wine+Quality
The last step is to populate the table using the wine dataset:
dbInsertTable(conn, "wine_clusters", read_csv("data.csv"))
Retrieve the Data from GridDB¶
In this section, we will retrieve the data from our database. We will use the SELECT query to complete this task, which returns all database values.
The following code was used to conduct the task explained in this section:
queryString <- "select Alcohol, Malic_Acid, Ash, Ash_Alcanity, Magnesium, Total_Phenols,
Flavanoids, Nonflavanoid_Phenols, Proanthocyanins, Color_Intensity,
Hue, OD280, Proline, Customer_Segment from wine_clusters"
To retrieve the data from our GridDB database, we can use the dbGetQuery method to do so:
data <- dbGetQuery(conn, queryString)
The Dataset¶
To take a quick look at our dataset, we will run the following code:
glimpse(data)
Rows: 178 Columns: 14 $ Alcohol <dbl> 14.23, 13.20, 13.16, 14.37, 13.24, 14.20, 14.39, … $ Malic_Acid <dbl> 1.71, 1.78, 2.36, 1.95, 2.59, 1.76, 1.87, 2.15, 1… $ Ash <dbl> 2.43, 2.14, 2.67, 2.50, 2.87, 2.45, 2.45, 2.61, 2… $ Ash_Alcanity <dbl> 15.6, 11.2, 18.6, 16.8, 21.0, 15.2, 14.6, 17.6, 1… $ Magnesium <dbl> 127, 100, 101, 113, 118, 112, 96, 121, 97, 98, 10… $ Total_Phenols <dbl> 2.80, 2.65, 2.80, 3.85, 2.80, 3.27, 2.50, 2.60, 2… $ Flavanoids <dbl> 3.06, 2.76, 3.24, 3.49, 2.69, 3.39, 2.52, 2.51, 2… $ Nonflavanoid_Phenols <dbl> 0.28, 0.26, 0.30, 0.24, 0.39, 0.34, 0.30, 0.31, 0… $ Proanthocyanins <dbl> 2.29, 1.28, 2.81, 2.18, 1.82, 1.97, 1.98, 1.25, 1… $ Color_Intensity <dbl> 5.64, 4.38, 5.68, 7.80, 4.32, 6.75, 5.25, 5.05, 5… $ Hue <dbl> 1.04, 1.05, 1.03, 0.86, 1.04, 1.05, 1.02, 1.06, 1… $ OD280 <dbl> 3.92, 3.40, 3.17, 3.45, 2.93, 2.85, 3.58, 3.58, 2… $ Proline <dbl> 1065, 1050, 1185, 1480, 735, 1450, 1290, 1295, 10… $ Customer_Segment <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
The attributes covered in this dataset are as follows:
- Alcohol this attribute is a numeric value.
- Malic Acid this attribute is a numeric value.
- Ash this attribute is a numeric value.
- Ash Alcanity this attribute is a numeric value.
- Magnesium this attribute is a numeric value.
- Total Phenols this attribute is a numeric value.
- Flavanoids this attribute is a numeric value.
- Nonflavanoid Phenols this attribute is a numeric value.
- Proanthocyanins this attribute is a numeric value.
- Color Intensity this attribute is a numeric value.
- Hue this attribute is a numeric value.
- OD280 this attribute is a numeric value.
- Proline this attribute is a numeric value.
- Customer Segment this attribute is a numeric value that takes three categories of segments 1, 2, and 3.
To display the first 5 rows of our dataset, we can use the head method to do so:
head(data, n = 5)
| Alcohol | Malic_Acid | Ash | Ash_Alcanity | Magnesium | Total_Phenols | Flavanoids | Nonflavanoid_Phenols | Proanthocyanins | Color_Intensity | Hue | OD280 | Proline | Customer_Segment |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> |
| 14.23 | 1.71 | 2.43 | 15.6 | 127 | 2.80 | 3.06 | 0.28 | 2.29 | 5.64 | 1.04 | 3.92 | 1065 | 1 |
| 13.20 | 1.78 | 2.14 | 11.2 | 100 | 2.65 | 2.76 | 0.26 | 1.28 | 4.38 | 1.05 | 3.40 | 1050 | 1 |
| 13.16 | 2.36 | 2.67 | 18.6 | 101 | 2.80 | 3.24 | 0.30 | 2.81 | 5.68 | 1.03 | 3.17 | 1185 | 1 |
| 14.37 | 1.95 | 2.50 | 16.8 | 113 | 3.85 | 3.49 | 0.24 | 2.18 | 7.80 | 0.86 | 3.45 | 1480 | 1 |
| 13.24 | 2.59 | 2.87 | 21.0 | 118 | 2.80 | 2.69 | 0.39 | 1.82 | 4.32 | 1.04 | 2.93 | 735 | 1 |
Data Analysis¶
Before storing or running our machine learning cluster, we will have to analyze the data. As a start, we will inspect the datatypes of our columns.
To conduct this task, we will run the following code:
str(data)
spc_tbl_ [178 × 14] (S3: spec_tbl_df/tbl_df/tbl/data.frame) $ Alcohol : num [1:178] 14.2 13.2 13.2 14.4 13.2 ... $ Malic_Acid : num [1:178] 1.71 1.78 2.36 1.95 2.59 1.76 1.87 2.15 1.64 1.35 ... $ Ash : num [1:178] 2.43 2.14 2.67 2.5 2.87 2.45 2.45 2.61 2.17 2.27 ... $ Ash_Alcanity : num [1:178] 15.6 11.2 18.6 16.8 21 15.2 14.6 17.6 14 16 ... $ Magnesium : num [1:178] 127 100 101 113 118 112 96 121 97 98 ... $ Total_Phenols : num [1:178] 2.8 2.65 2.8 3.85 2.8 3.27 2.5 2.6 2.8 2.98 ... $ Flavanoids : num [1:178] 3.06 2.76 3.24 3.49 2.69 3.39 2.52 2.51 2.98 3.15 ... $ Nonflavanoid_Phenols: num [1:178] 0.28 0.26 0.3 0.24 0.39 0.34 0.3 0.31 0.29 0.22 ... $ Proanthocyanins : num [1:178] 2.29 1.28 2.81 2.18 1.82 1.97 1.98 1.25 1.98 1.85 ... $ Color_Intensity : num [1:178] 5.64 4.38 5.68 7.8 4.32 6.75 5.25 5.05 5.2 7.22 ... $ Hue : num [1:178] 1.04 1.05 1.03 0.86 1.04 1.05 1.02 1.06 1.08 1.01 ... $ OD280 : num [1:178] 3.92 3.4 3.17 3.45 2.93 2.85 3.58 3.58 2.85 3.55 ... $ Proline : num [1:178] 1065 1050 1185 1480 735 ... $ Customer_Segment : num [1:178] 1 1 1 1 1 1 1 1 1 1 ... - attr(*, "spec")= .. cols( .. Alcohol = col_double(), .. Malic_Acid = col_double(), .. Ash = col_double(), .. Ash_Alcanity = col_double(), .. Magnesium = col_double(), .. Total_Phenols = col_double(), .. Flavanoids = col_double(), .. Nonflavanoid_Phenols = col_double(), .. Proanthocyanins = col_double(), .. Color_Intensity = col_double(), .. Hue = col_double(), .. OD280 = col_double(), .. Proline = col_double(), .. Customer_Segment = col_double() .. ) - attr(*, "problems")=<externalptr>
The next step is to provide summary statistics for every column we have in the wine dataset using the following code:
summary(data)
Alcohol Malic_Acid Ash Ash_Alcanity
Min. :11.03 Min. :0.740 Min. :1.360 Min. :10.60
1st Qu.:12.36 1st Qu.:1.603 1st Qu.:2.210 1st Qu.:17.20
Median :13.05 Median :1.865 Median :2.360 Median :19.50
Mean :13.00 Mean :2.336 Mean :2.367 Mean :19.49
3rd Qu.:13.68 3rd Qu.:3.083 3rd Qu.:2.558 3rd Qu.:21.50
Max. :14.83 Max. :5.800 Max. :3.230 Max. :30.00
Magnesium Total_Phenols Flavanoids Nonflavanoid_Phenols
Min. : 70.00 Min. :0.980 Min. :0.340 Min. :0.1300
1st Qu.: 88.00 1st Qu.:1.742 1st Qu.:1.205 1st Qu.:0.2700
Median : 98.00 Median :2.355 Median :2.135 Median :0.3400
Mean : 99.74 Mean :2.295 Mean :2.029 Mean :0.3619
3rd Qu.:107.00 3rd Qu.:2.800 3rd Qu.:2.875 3rd Qu.:0.4375
Max. :162.00 Max. :3.880 Max. :5.080 Max. :0.6600
Proanthocyanins Color_Intensity Hue OD280
Min. :0.410 Min. : 1.280 Min. :0.4800 Min. :1.270
1st Qu.:1.250 1st Qu.: 3.220 1st Qu.:0.7825 1st Qu.:1.938
Median :1.555 Median : 4.690 Median :0.9650 Median :2.780
Mean :1.591 Mean : 5.058 Mean :0.9574 Mean :2.612
3rd Qu.:1.950 3rd Qu.: 6.200 3rd Qu.:1.1200 3rd Qu.:3.170
Max. :3.580 Max. :13.000 Max. :1.7100 Max. :4.000
Proline Customer_Segment
Min. : 278.0 Min. :1.000
1st Qu.: 500.5 1st Qu.:1.000
Median : 673.5 Median :2.000
Mean : 746.9 Mean :1.938
3rd Qu.: 985.0 3rd Qu.:3.000
Max. :1680.0 Max. :3.000
To understand the distribution of our dataset and determine if any patterns will enable us to create clusters, we will develop a set of histograms for every one of our columns.
This task can be coded as follows:
data %>% keep(is.numeric) %>% gather() %>% ggplot(aes(value)) +
facet_wrap(~ key, scales = "free") + geom_histogram(bins = 30)
Another viewpoint to take while analyzing the data is determining the number of customers based on our three segments. Based on our analysis, these values are close from a numerical perspective. For the first segment, we have a total of 59 rows. The following customer, referred to as number 2, is the high proportion of our dataset with 79 rows. Last but not least, the final segment is totaled to a total number of 48 rows. The ratio of customer segments is evenly matched without any of the segments being exceptionally high compared to the others.
The code that performs this task in R is as follows:
data %>%
group_by(Customer_Segment) %>%
summarise(count = n())
| Customer_Segment | count |
|---|---|
| <dbl> | <int> |
| 1 | 59 |
| 2 | 71 |
| 3 | 48 |
Implementing a K-means Clustering in R¶
This article will employ a k-means cluster. To explain, this unsupervised machine learning model creates groups of attributes based on similarities in a dataset. To simplify, our model will create wine groups based on acidity, flavor, and other chemical alcohol features.
To run a K-means model using R programming, we will have first to normalize our data frame using the scale method as follows:
df_normalize <- as.data.frame(scale(data))
Build the K-means Clustering¶
The K-means Clustering model will output the different wine groups based on their chemical similarities in our R program. Our code will first initialize the K-means Cluster object. The K-means Clustering algorithm will then be set up using the standard parameters.
km.res <- kmeans(df_normalize, 3, nstart = 25)
Conclusion & Results¶
The final section of our article is the understanding of our results. To easily digest how well our K-means Cluster successfully outputted the wine groups based on their chemical analysis. The main number we shall focus on in this section is the number of clusters. Which represents the total number of wine groups discovered using our K means algorithm. Our K-means Cluster outputted three groups. To explain, this is considered a great result as we can identify the edges of each group and their dimensions from the graph. For future development, our model will fully use the GridDB database as it provides an easy input-output data interface and an incredible speed of data retrieval.
The following cluster plot is a representation of our results:
fviz_cluster(km.res, data = df_normalize, ellipse.type = "convex")
