## Introduction

Since both K-means Clustering and Hierarchical Clustering have the same continuous variable they needed a description raw. This description will be made for the continuous variables. After that description, they are going to be analyzed individually. Their comparison will be made in the third phase. With that being said, Beats per infinitesimal and Danceability were chosen as the continuous variables. The argue is that is a handful of researches that are about beats per minute and danceability ( Herremans, Martens & Sörensen, 2014 ; Goel et. alabama, 2014 ; Jamdar et. aluminum, 2015 ; Elías Alonso, 2016 ; Howlin & Rooney, 2020, 2021 ; Kim, Aiello & Quercia, 2020 ; Krishna et. alabama, n.d. ). besides, those researchers are in different fields of the academy. Some of these articles are directly related to K-means ( Kim, Aiello & Quercia, 2020 ) and hierarchical Clustering ( Kusama & Itoh, 2011, 2014 ; King & Imbrasaitė, 2015 ). And finally, Principal Component Analysis will be applied. Since academic references were given about other methods, PCA can not be left alone. In one study, music genres were examined on the footing of PCA. They suggested that the artists within the genres should specifically raise the index value to make their works more democratic ( Long, Hu & Jin, 2021 ). In other research, Luo ( 2018 ) divided audio features into sets that are linearly uncorrelated to others. She collected her data from Kaggle.com, Spotify Web API, and Discogs AIPs. To divide audio features, she implements PCA. In another study, PCA, K-means, and Hierarchical Clustering were being used. One of the findings of PCA in that article is ; projections onto 2D images showed that the use of PCA with 20 components ( maintaining about 80 % discrepancy ) well improved the bunch models ( Langensiepen, Cripps & Cant, 2018 ). All in all, this learn aims to shed some unaccented on K-means, Hierarchical Clustering, Principal Component Analysis .

## K-means Clustering

## Output of K-means

### Comments

The measure of the total variance is explained by the cluster is 68.9 %. The mean values are close to each early for Danceability. It can be stated that the mean values of BPM varied highly. possibly, because, BPM rates can reach 200 and Danceability score entirely reaches 100. Another argue for the differentiation of BPM and Danceability is ; the number is about the peak 50 most listened to songs in the world by Spotify. thus, the song categories or genres differentiated by listeners ‘ choices. One exemplar about the choices, it would not fair to compare Electronic music ( Hörschläger et. aluminum, 2015 ; Alspach, 2020 ) to romanticist music ( Pérez Lloret et. aluminum, 2014 ) types in terms of their BPM. One suggestion here is that standardization could be applied ( Dodge & Commenges, 2003 ). Because the difference is not equitable between BPM and Danceability. There are some variables ( Loudness DB ) in this dataset that their scores are below zero, or there are some variables ( Length ) that their scores are more than 300 .

## GGPlot

### Comments

The first observation is 125 beats per minute is the partition point. The former interpretations can be seen here visually. There are two interest variables here. One of them has 190 BPM. But hush, its Danceability score is 40. The early one has 85 BPM. But its Danceability is 29. The first one might be interpreted by its BPM score. Because after some point people might not want to dance. The second one is that even if that song has 85 BPM, 29 Danceability is a bite foreign. People dance in romanticist songs besides. A refer argument is that these songs are listed crown 50 in Spotify. so, how come those two songs have the lowest Danceability scores ? Danceability definition might help the readers here. The writer was extracted his data from OrganizeYourMusic. The web site describes Danceability with the given words ; the higher the value, the easier it is to dance to this sung. therefore, this explanation did not help besides. The dataset can be examined. The song that has 190 BPM belongs to Ariana Grande. The song ‘s class is dance-pop. The other birdcall belongs to The Chainsmokers. The song ‘s category is “ EDM ”, which means that Electronic dance music. This song has 85 BPM, 29 Danceability, and it was categorized as electronic dance music. Whoever categorized those songs deserves criticism. In fairness to Danceability score, one should n’t expect others to dance to every song they listen to. Since standardization was applied anterior to performing Hierarchical Clustering, the lapp process will be applied K-means Clustering besides. This process is important to be clean to Hierarchical Clustering.

## Normalization of K-means

The measure of the total variance is explained by the cluster is 39.7 % .

### GGPlot for Normalization

It seems that the graph obtained from K-means Clustering is the about identical previous one. It looks like the scale did not change the output .

## Hierarchical Clustering

Before applying Hierarchical Clustering to this dataset, variables are normalized .

## Plots

### Comments

Single linkage is confusing in terms of how the variables are clustered. The lapp statement can be made for average linkage besides. complete Linkage, on the early hand, seems more meet than the other two. To clarify this gloss, the songs ‘ numbers and their BPM/Danceability scores need to be examined. It looks like the left side of the dendrogram is for Danceability, and the right side of the dendrogram is for BPM. Number 36 is Martin Garrix, and its Danceability is 66. Number 21 is Martin Garrix, and its Danceability is 66. Number 26 is Shawn Mendes, and its Danceability is 69. Number 10 is Billie Eilish, and its Danceability is 70. Number 25 is Billie Eilish, and its Danceability is 67. The examples of another slope of the aisle as follows ; Number 3 is Ariana Grande, and its BPM is 190. Number 14 is Sech, and its BPM is 176. Number 7 is Lil Tecca, and its BPM is 180. Number 17 is J Balvin, and its BPM is 176. Number 37 is Sech, and its BPM is 176. so, complete linkage divides the songs in terms of some of the highest Danceability and BPM. On the left side, there is an matter to postpone which is about the same artists ‘ songs. Martin Garrix and Billie Eilish had two songs slope by side. How hail Hierarchical Clustering recognized those two people ‘s work without knowing their other information ? Could that be a coincidence ? On the right side, the accuracy of Hierarchical Clustering is at its extremum. At least, it can be stated that Hierarchical Clustering is ordered the songs about their BPM. The examen has started from the leave side. 36,21,26,10 and 25 are K-means Clustering. 3,14,7,17 and 37 are hierarchical Clustering .

## Cutting Tree for 2 Clusters

### Complete Linkage

Cluster 1 is 28 and Cluster 2 is 22 .

### Average Linkage

Cluster 1 is 49 and Cluster 2 is 1 .

### Single Linkage

Cluster 1 is 49 and Cluster 2 is 1. The rendition given above can be seen when cutting trees. complete Linkage separated the observations into their highest scores. besides, to obtain a more reasonable answer, 4 clusters are going to be examined .

## Cutting Tree for 4 Clusters

### Complete Linkage

Cluster 1 is 28, Cluster 2 is 11, Cluster 3 is 10, and Cluster 4 is 1 .

### Average Linkage

Cluster 1 is 30, Cluster 2 is 1, Cluster 3 is 16, and Cluster 4 is 3 .

### Single Linkage

Cluster 1 is 47, Cluster 2 is 1, Cluster 3 is 1, and Cluster 4 is 1. To grasp these output given above ( cutting a tree ), complete, average, and unmarried linkages are going to be drawn for 4 clusters . After a few tries, it is clear that complete Linkage separated the observations into their highest scores. On the other hand, finding a common grind for classification of modal linkage and Single Linkage is more than unmanageable. besides, Complete Linkage is fairer than the early two when it comes to separating the observations .

## Comparison of K-means Clustering and Hierarchical Clustering.¶

In terms of reading the variables from the graphics, Hierarchical Clustering has advantages over K-means Clustering. The most authoritative advantage is the count of songs. This insight creates many paths for the analyzer. For model, finding Martin Garrix ( 21 and 36 ) and Billie Eilish ( 10 and 25 ) in Hierarchical Clustering can clarify many details. Another example is hierarchical Clustering starts from higher numbers of BPM and Danceability. Related review can be capable here. Some people may argue that starting from the highest score might not be the expect approach. speak of the highest scores, K-means only divided this dataset into two parts. But this divide makes interpretation unmanageable. For that argue, in the interpretation part, HC is more accurate. Meaning, one can interpret accurately when one uses HC. In fairness to both K-means Clustering and Hierarchical Clustering, in one report, Long, Hu, and Jin ( 2021 ) conclude their article with these words ; “ the higher the energy, cornice, tempo, count, and enliven indexes are, the more the birdcall fits the characteristics of the Pop/Rock music genre and the more popular it is by patriotic fans in the field. ” The mean of those words is there are merely two variables in this examination. K-means Clustering and Hierarchical Clustering might have needed more indexes to cluster these songs .

## PCA

## Mean Values for the Indexes

Beats.Per.Minute:120.06 Energy : 64.06 Danceability : 71.38 Loudness..dB.. : -5.66 Liveness : 14.66 valence. : 54.6 Length. : 200.96 Acousticness.. : 22.16 Speechiness. : 12.48 popularity : 87.5 The mean values of the variables varied. Meaning, Loudness is below zero. Length is 201 .

## Variance Values for the Indexes

Beats.Per.Minute : 954.710612244898 Energy : 202.547346938776 Danceability:142.322040816327 Loudness..dB.. : 4.22897959183673 animateness : 123.616734693878 valence. : 498.897959183673 Length. : 1532.24326530612 : Acousticness.. : 360.831020408163 Speechiness. : 124.581224489796 popularity : 20.1734693877551 The same interpretation can be made here. The variance of Length and Beats Per Minute are so high. They can dominate the early variables. As stated above the variables are going to be standardized, and then PCA will be performed .

## Output of PCA

### Comments on the Components

In PC1, 7 seven loadings on the minus side. It seems that they can be labeled for PC1. But one looks closer, BPM, Acousticness, and Popularity are close to each other. besides, some of the highest scores in each load found themselves a board in the top 50 list. For example, number 13 is made by Lewis Capaldi, the birdcall ‘s BPM is 110, Acousticness is 75, and Popularity is 88. Number 27 is made by Tones and I. The birdcall ‘s BPM is 98, Acousticness is 69, and Popularity is 83. Number 11 is made by Bad Bunny. The song ‘s BPM is 176, Acousticness is 60, and Popularity is 93. To clarify these examples, the highest scores for BPM is 190, Acousticness is 75, and Popularity is 95. In PC2, again, 7 loadings on the negative english. BPM and Speechiness can be labeled here. They have high minus loadings. besides, some of the highest scores in BPM are besides some of the highest scores in Speechiness excessively. Meaning, the highest scores on both BPM and Speechiness go parallel. For case, number 3 is made by Ariana Grande. The sung ‘s BPM is 190, and Speechiness is 46. Number 11 is made by J Balvin. The birdcall ‘s BPM is 176, and Speechiness is 34. Number 7 is made by Lil Tecca. The song ‘s BPM is 180, and Speechiness is 29. In PC3, loadings are divided equally. Meaning, there are 5 positive loadings, and there are 5 negative loadings. Danceability and Valence come as the highest plus loadings. Valence needs an introduction here. The first visit was made to Organize Your Music. Because the generator was extracted his data from this web site. They explained Valence as the higher the value, the more positive temper for the song. In one sketch, Valence explained as valence covers the space between unpleasant ( e.g. sad, stressed ) to delightful ( e.g. cheerful, elated ) ( Shahnaz & Hasan, 2016 ). besides, one of the methods in this research is PCA. sol, that makes Valence definition impregnable. As in the first two components, selected loadings had related highest scores. Number 35 is sung by ROSALÍA. The sung ‘s Danceability is 88, and its Valence is 75. Number 9 is sung by Lil Nas X. The song ‘s Danceability is 88, its Valence is 64. Number 39 is sung by Jonas Brothers. The birdcall ‘s Danceability is 84, its Valence is 95. Another stronger argument that Danceability and Valence selected/labeled in PC3 is ; higher Valences are described by Organize Your Music and Shahnaz and Hasan ( 2016 ) with positivist words. From that explanation, it can be stated that people dance to songs that positively affect them. In PC4, again, 7 loadings on the negative side. Selecting loadings in this part a bit crafty. Beats Per Minute and Liveness can be selected here. Because their loading scores are close to each other. But when analyzing the loadings one can see that this part, on the other pass, produces an index that shows Acousticness more. Four components seemed adequate. Why blockage at the 4th component will be explained at the end of the paper .

## BiPlot

According to the graphic, Beats for Minute, Speechiness and Popularity go in the same direction for PC1 ; Acousticness goes in another direction for PC1. In the previous subject, it was stated that BPM, Acousticness, and Popularity can be selected for labeling the first part. however, the solution of the biplot is different. Speechiness took Acousticness ‘ position. In the former topic, it was stated that BPM and Speechiness can be selected for labeling the second component. The biplot validates that suggestion. One interpretation can be added according to the biplot. Loudness and Energy can be pronounce factors for PC2. On the other hand, BPM and Speechiness have high minus loadings than Loudness and Energy.

Read more: Top 10 Best Songs of All Time

## Proportion of Variance Explained

0.231143413332697 0.164303321557439 0.137106109520043 0.105283062503717 0.0977312018040011 0.0836911427025456 0.0716513481690877 0.0547537154600018 0.0319672217213592 0.0223694632291085 The first principal component explains 23.11 % of the variability. The irregular chief component explains 16.43 % of the discrepancy. The third gear one explains 13.71 %, and the fourth one explains 10.52 %. Four components jointly accounted for 63.78 % of the total variance ( Carlson et alabama. 2017 ) .

## Plots and Comments on the Results

The inaugural principal component explains 23.11 % of the division . As stated above four components seemed enough, and they will be explained at the end of the newspaper. Holland ( 2008 ) asked the interview of how many PCs should be ignored. The standard he stated as follows ; “ 1 ) One common standard is to ignore principal components at the point at which the following personal computer offers little increase in the total division explained. 2 ) A moment standard is to include all those PCs up to a bias sum percentage discrepancy explained, such as 90 %. 3 ) A third standard is to ignore components whose variation explained is less than 1 when a correlation matrix is used or less than the average variance explained when a covariance matrix is used, with the estimate being that such a personal computer offers less than one varying ’ s deserving of information. 4 ) A fourth standard is to ignore the last PCs whose variance explained is all roughly adequate. ” In the criteria he stated, the most feasible one for this study is 4. Because PC5 is 0.09773120, PC6 is 0.08369114, PC7 is 0.07165135, PC8 is 0.05475372, PC9 is 0.03196722, and last, PC10 is 0.02236946. PC4 and PC5 ( and the stick to ) are not selected. Because the first base four components are not roughly equal to each other. PC5, PC6, PC7, and therefore forth, are roughly adequate. They are so stopping point to being adequate. To sum it up, four components will be selected. One suggestion might be added hera for combining the loadings. BPM, Speechiness, Popularity, Danceability, Valence, and Acousticness indexes might be the factors for entering the top music lists ( Long, Hu & Jin, 2021 ). credit for the dataset : **Leonardo Henrique**

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