euclidean distance formula in data mining

The formula for Minkowski distance is: D(x,y) = p√Σd|xd –yd|p Here we can see that the formula differs from the formula of Euclidean distance as we can see that instead of squaring the difference, we have raised the difference to the power of p and have also taken the p root of the difference. Metode Clustering memiliki tujuan utama mengelompokkan data berdasarkan suatu nilai 'kemiripan' (sering disebut juga similarity) yang dimiliki oleh data-data tersebut. The formula is shown below: Manhattan Distance Measure. The Manhattan distance between two items is the sum of the differences of their corresponding components. Most clustering approaches use distance measures to assess the similarities or differences between a pair of objects, the most popular distance measures used are: 1. This is a surprising result in light of the fact that the Euclidean distance metric is traditionally used in a large variety of indexing structures and data mining applications. They are subsetted by their label, assigned a different colour and label, and by repeating this they form different layers in the scatter plot.Looking at the plot above, we can see that the three classes are pretty well distinguishable by these two features that we have. The raw Euclidean distance for these data is: 100.03. Don’t stop learning now. The Jaccard distance measures the similarity of the two data set items as the intersection of those items divided by the union of the data items. Experience. For example, similarity among vegetables can be determined from their taste, size, colour etc. We can repeat this calculation for all pairs of samples. Let's look at some examples, for the same data sets, we get a four points. Euclidean Distance Formula. 3. λ→∞:L∞metric, Supremum distance. Euclidean distance (sameed, sameed) = SQRT (   (X1 – X2)2 + (Y1 -Y2)2   ) = 0, Euclidean distance (sameed, sameed) = SQRT ( (10 – 10)2 + (90 -90)2) = 0, Here note that (90-95) = -5 and when we take sqaure of a negative number then it will be a positive number. Suppose we have two points P and Q to determine the distance between these points we simply have to calculate the perpendicular distance of the points from X-Axis and Y-Axis. In the limit that p --> +infinity, the distance is known as the Chebyshev distance. Manhattan distance between P and Q = |x1 – x2| + |y1 – y2|. This requires a distance measure, and most algorithms use Euclidean Distance or Dynamic Time Warping (DTW) as their core subroutine. Abstract: At their core, many time series data mining algorithms can be reduced to reasoning about the shapes of time series subsequences. Some of the popular similarity measures are – Euclidean Distance. We’ll first put our data in a DataFrame table format, and assign the correct labels per column:Now the data can be plotted to visualize the three different groups. Since the distance … Euclidean Distance: share | improve this answer | follow | answered Oct 14 '18 at 18:00. Euclidean Distance The Euclidean distance formula is used to measure the distance in the plane. The Dissimilarity matrix is a matrix that expresses the similarity pair to pai… Therefore it would not be possible to calculate the distance between a label and a numeric point. Amazon has this section called “customers that bought this item alsobought”, which is self-explanatory 3. a service like IMDB, based on your ratings, could find users similarto you, users that l… In a Data Mining sense, the similarity measure is a distance with dimensions describing object features. Difference Between Data Mining and Text Mining, Difference Between Data Mining and Web Mining, Difference between Data Warehousing and Data Mining, Difference Between Data Science and Data Mining, Difference Between Data Mining and Data Visualization, Difference Between Data Mining and Data Analysis, Difference Between Big Data and Data Mining, Basic Concept of Classification (Data Mining), Frequent Item set in Data set (Association Rule Mining), Redundancy and Correlation in Data Mining, Attribute Subset Selection in Data Mining, Data Structures and Algorithms – Self Paced Course, We use cookies to ensure you have the best browsing experience on our website. DATA MINING USING AGGLOMERATIVE MEAN SHIFT CLUSTERING WITH EUCLIDEAN DISTANCE. We get two dimensions. It can be simply explained as the ordinary distance between two points. 3. For more information on algorithm … The Euclidean Distance procedure computes similarity between all pairs of items. It is a symmetrical algorithm, which means that the result from computing the similarity of Item A to Item B is the same as computing the similarity of Item B to Item A. Point 1: 32.773178, -79.920094 Point 2: 32.781666666666666, -79.916666666666671 Distance: 0.0091526545913161624 I would like a fairly simple formula for converting the distance to feet and meters. The following example shows score when comparing the first sentence. If K=1 then the nearest neighbor is the last case in the training set with Default=Y. It is the generalized form of the Euclidean and Manhattan Distance Measure. Given this, we believe that the MPdist may have a similar impact on time series data mining … Euclidean Distance & Cosine Similarity – Data Mining Fundamentals Part 18 Data Science Dojo January 6, 2017 6:00 pm Euclidean distance and cosine similarity are the next aspect of similarity and dissimilarity we will discuss. Cosine Similarity. The Minkowski distance is a generalization of the Euclidean distance. Dissimilarity may be defined as the distance between two samples under some criterion, in other words, how different these samples are. For example from x2 to x1 you will go three blocks down then two blocks left. The raw Euclidean distance is now: 2.65. ... TF IDF Cosine similarity Formula Examples in data mining; Distance measure for asymmetric binary; Distance measure for symmetric binary; Euclidean distance; Classification; C4.5; KNN algorithm in data mining with examples; Clustering; Association rule mining; Regression; MCQs; attribute selection measure; euclidean distance; Variance … Euclidean distance is the shortest distance between two points in an N dimensional space also known as Euclidean space. To calculate the distance between two points (your new sample and all the data you have in your dataset) is very simple, as said before, there are several ways to get this value, in this article we will use the Euclidean distance. Salah satu teknik untuk mengukur kemiripan suatu data dengan data lain adalah dengan mencari nilai Euclidean Distance (ED) kedua data tersebut. For most common clustering software, the default distance measure is the Euclidean distance. The way that various distances are often calculated in Data Mining is using the Euclidean distance. Jaccard Index: Clustering consists of grouping certain objects that are similar to each other, it can be used to decide if two items are similar or dissimilar in their properties. Euclidean Distance & Cosine Similarity | Introduction to Data … The Euclidean distance can only be calculated between two numerical points. Latest posts by Prof. Fazal Rehman Shamil, Euclidean distance (sameed, sameed) = SQRT ( (10 – 10), Euclidean distance (sameed, shah zeb) = SQRT ( (10 – 6), Euclidean distance (shah zeb, sameed) = SQRT ( (10 – 6), Comparison of fee structure of Pakistani Universities, TF IDF Cosine similarity Formula Examples in data mining, KNN algorithm in data mining with examples, Analytical Characterization in Data Mining, Data Generalization In Data Mining – Summarization Based Characterization, Proximity Measure for Nominal Attributes –, Distance measure for asymmetric binary attributes –, Distance measure for symmetric binary variables –, Jaccard coefficient similarity measure for asymmetric binary variables –. Normalization, which scales all numeric variables in the range [0,1]. We don’t compute the … Euclidean distance can be generalised using Minkowski norm also known as the p norm. Suraj s. Damre 1,prof.L.M.R.J. ... data mining, deep learning, and others. Depending on the type of the data and the researcher questions, … The Manhattan distance function computes the distance that would be traveled to get from one data point to the other if a grid-like path is followed. This algorithm is in the alpha tier. Now the biggest advantage of using such a distance metric is that we can change the value of p to get different types of distance metrics. The raw Euclidean distance is now: 2.65. The way that various distances are often calculated in Data Mining is using the Euclidean distance. We call this the standardized Euclidean distance , meaning that it is the Euclidean distance calculated on standardized data. This file contains the Euclidean distance of the data after the min-max, decimal scaling, and Z-Score normalization. Thanks! 2. λ=2:L2metric, Euclidean distance. Euclidean distance measures the straight line distance between two points in n-dimensional space. 1,047 4 4 gold badges … It is a very famous way to get the distance … acknowledge that you have read and understood our, GATE CS Original Papers and Official Keys, ISRO CS Original Papers and Official Keys, ISRO CS Syllabus for Scientist/Engineer Exam, Movie recommendation based on emotion in Python, Python | Implementation of Movie Recommender System, Item-to-Item Based Collaborative Filtering. Manhattan distance: Manhattan distance is a metric in which the distance between two points is … ... TF IDF Cosine similarity Formula Examples in data mining; Distance measure for asymmetric binary; Distance measure for symmetric binary; Euclidean distance; Classification; C4.5; KNN algorithm in data mining with examples; Clustering; Association rule mining; Regression; MCQs ; … It uses Pythagorean Theorem which learnt from secondary school. One possible formula is given below: In an N-dimensional space, a point is represented as. — p 135, Data Mining Practical Machine Learning Tools and Techniques (4th edition, 2016). I just need a formula that will get me 95% there. Age and Loan are two numerical variables (predictors) and Default is the target. and a point Y =(Y 1, Y 2, etc.) 1 Department of Computer Science, Walchand Institute of technology, Solapur, Maharashtra. The Manhattan distance is the simple sum of the horizontal and … So the Manhattan distance is 3 plus 2, we get 5, … It measures the numerial difference for each corresponding attributes of point p and point q. Overview of Scaling: Vertical And Horizontal Scaling, SQL | Join (Inner, Left, Right and Full Joins), Commonly asked DBMS interview questions | Set 1, Introduction of DBMS (Database Management System) | Set 1, Python | Scipy stats.halfgennorm.fit() method, Generalization, Specialization and Aggregation in ER Model, Types of Keys in Relational Model (Candidate, Super, Primary, Alternate and Foreign), Difference between DELETE, DROP and TRUNCATE, Write Interview One may also ask, how do you calculate Supremum distance? Here the total distance of the Red line gives the Manhattan distance between both the points. Although there are other possible choices, most instance-based learners use Euclidean distance. It will be assumed that standardization refers to the form defined by (4.5), unless specified otherwise. The Euclidean Distance procedure computes similarity between all pairs of items. In the formula above, x and y are two vectors of length n and, means \ (\bar{x}\) and \(\bar{y}\), respectively. 4. The basis of many measures of similarity and dissimilarity is euclidean distance. 2. When to use cosine similarity over Euclidean similarity? I will explain the KNN algorithm with the help of the "Euclidean Distance" formula. The distance between vectors X and Y is defined as follows: In other words, euclidean distance is the square root of the sum of squared differences between corresponding elements of the two vectors. The Euclidean distance can only be calculated between two numerical points. This determines the absolute difference among the pair of the coordinates. Euclidean distance is considered the traditional metric for problems with geometry. Note that the formula treats the values of X and Y seriously: no adjustment is made for differences in scale. In a plane with P at coordinate (x1, y1) and Q at (x2, y2). The raw Euclidean distance for these data is: 100.03. Because it is a formalization of the “Pythagorean” theorem, this is … Lobo 2. [ 3 ] where n is the number of dimensions. Euclidean distance is the shortest distance between two points in an N-dimensional space also known as Euclidean space. It is widely used in pattern recognization, data mining, etc. It is a symmetrical algorithm, which means that the result from computing the similarity of Item A to Item B is the same as computing the similarity of Item B to Item A. In Data Mining, similarity measure refers to distance with dimensions representing features of the data object, in a dataset. Score means the distance between two objects. The resulting distance matrix can be fed further to Hierarchical Clustering for uncovering groups in the data, to Distance Map or Distance Matrix for visualizing the distances (Distance Matrix can be quite slow for larger data sets), to MDS for mapping the data … If I understand your question correctly, the answer is no. Similarity metric is the basic measurement and used by a number of data ming algorithms. If we had expressed the scores for variable 5 in the same metric as the other scores (on a 1‐10 metric scale), we would have scores of 1.2 and 1.3 respectively for each individual. I have a tool that outputs the distance between two lat/long points. When p=2, the distance is known as the Euclidean distance. The similarity is subjective and depends heavily on the context and application. What type of distance measures should we choose? Cosine Index: It is one of the most used algorithms in the cluster analysis. If this distance is less, there will be a high degree of similarity, but when the distance is large, there will be a low degree of similarity. That means if the distance among two data points is small then there is a high degree of similarity among the objects and vice versa. Two methods are usually well known for rescaling data. In … We can now use the training set to classify an unknown case (Age=48 and Loan=$142,000) using Euclidean distance. We can therefore compute the score for each pair of nodes once. … Ethan Ethan. The choice of distance measures is very important, as it has a strong influence on the clustering results. d(p, q) ≥ 0 for all p and q, and d(p, q) = 0 if and only if p = q,; d(p, q) = d(q,p) for all p and q,; d(p, r) ≤ d(p, q) + d(q, r) for all p, q, and r, where d(p, q) is the distance (dissimilarity) between points (data objects), p and q. When p=1, the distance is known as the Manhattan distance. Python | How and where to apply Feature Scaling? The maximum such absolute value of the distance, is the distance of L infinity norm or supremum distance. To find similar items to a certain item, you’ve got to first definewhat it means for 2 items to be similar and this depends on theproblem you’re trying to solve: 1. on a blog, you may want to suggest similar articles that share thesame tags, or that have been viewed by the same people viewing theitem you want to compare with 2. Cosine distance measure for clustering determines the cosine of the angle between two vectors given by the following formula. p … Attention reader! For example, (-5)2 = 25, Euclidean distance (sameed, shah zeb) = SQRT ( (10 – 6)2 + (90 -95)2) = 6.40312, Euclidean distance (shah zeb, sameed) = SQRT ( (10 – 6)2 + (90 -95)2) = 6.40312. This file contains the Euclidean distance of the data after the min-max, decimal scaling, and Z-Score normalization. Euclidean distance is the easiest and most obvious way of representing the distance between two points. D = Sqrt[(48-33)^2 + (142000-150000)^2] = 8000.01 >> Default=Y . Euclidean distance Euclidean distance is the shortest distance between two points in an N-dimensional space also known as Euclidean space. Let’s see the “Euclidean distance after the min-max, decimal scaling, and Z-Score normalization”. It stores all the available cases from the training dataset and classifies the new cases based on distance function. Email:[email protected]. Euclidean Distance . In mathematics, the Euclidean distance between two points in Euclidean space is the length of a line segment between the two points. This is identical to the Euclidean distance measurement but does not take the square root at the end. Consider the following data concerning credit default. The formula for this distance between a point X =(X 1, X 2, etc.) For most common clustering software, the default distance measure is the Euclidean … It is also called the Lλmetric. We don’t compute the … is: Where n is the number of variables, and X i and Y i are the … Euclidean distance is a technique used to find the distance/dissimilarity among objects. Dimension of the data matrix remains finite. 2 Department of Information technology, Walchand Institute of technology, Solapur , Maharashtra. The Euclidean distance’s formule is like the image below: Here (theta) gives the angle between two vectors and A, B are n-dimensional vectors. ABSTRACT: Agglomerative clustering is a non … This is an old post, but just want to explain that the squaring and square rooting in the euclidean distance function is basically to get absolute values of each dimension assessed. It can be calculated from the Cartesian coordinates of the points using the Pythagorean theorem, therefore occasionally being called the Pythagorean distance.These names come from the ancient Greek mathematicians Euclid and Pythagoras, although Euclid did not … By using our site, you We can now use the training set to classify an unknown case (Age=48 and Loan=$142,000) using Euclidean distance. Therefore, all parameters should have the same scale for a fair comparison between them. You can read about that further here. It is usually non-negative and are often between 0 and 1, where 0 means no similarity, and 1 means complete similarity. 1. λ=1:L1metric, Manhattan or City-block distance. • While a single comparison is expense (relative to Euclidean distance), the amortized cost of subsequence search is relatively cheap, essentially the same as Euclidean distance. Informally, the similarity is a numerical measure of the degree to which the two objects are alike. If we had expressed the scores for variable 5 in the same metric as the other scores (on a 1‐10 metric scale), we would have scores of 1.2 and 1.3 respectively for each individual. It measures the similarity or dissimilarity between two data objects which have one or multiple attributes. Euclidean Distance: is the distance between two points (p, q) in any dimension of space and is the most common use of distance.When data is dense or continuous, this is the best proximity measure. Manhattan Distance: Minkowski distance: … The distance between x and y is denoted d(x, y). If it is 0, it means that both objects are identical. The formula for distance between two points is shown below: Squared Euclidean Distance Measure. The Dissimilarity index can also be defined as the percentage of a group that would have to move to another group so the samples to achieve an even distribution. It is used as a common metric to measure the similarity between two data points and used in various fields such as geometry, data mining, deep learning and others. Writing code in comment? Minkowski distance: It is the generalized form of the Euclidean and Manhattan Distance Measure. Sparse data can only be used with Euclidean, Manhattan and Cosine metric. … Then we look at the Manhattan distance is just a city block distance. Comparing the shortest distance among two objects. Then it combines the square of differencies in each dimension into an overal distance. Then, the Minkowski distance between P1 and P2 is given as: 5. Manhattan Distance. limλ→∞=(∑pk=1|xik−xjk|λ)1λ=max(|xi1−xj1|,...,|xip−xjp|) Note that λ and p are two different parameters. It is the distance between the two points in Euclidean space. The formula of Euclidean distance is as following. Distance, such as the Euclidean distance, is a dissimilarity measure and has some well-known properties: Common Properties of Dissimilarity Measures. [ 1 ] Considering different data type with a number of attributes, it is important to use the appropriate sim… We can therefore compute the score for each pair of nodes once. Considering the Cartesian Plane, one could say that the euclidean distance between two points is the measure of their dissimilarity. Age and Loan are two numerical variables (predictors) and Default is the target. Get hold of all the important CS Theory concepts for SDE interviews with the CS Theory Course at a student-friendly price and become industry ready. Default is the Euclidean distance ( 4th edition, 2016 ) dissimilarity is Euclidean distance just! See the “ Euclidean distance measure is a formalization of the Euclidean distance can only calculated! Pair of nodes once at coordinate ( x1, y1 ) and euclidean distance formula in data mining = |x1 – x2| + |y1 y2|... It uses Pythagorean theorem which learnt from secondary school KNN algorithm with the help the! Representing the distance between a point X = ( X, Y ) let ’ s see “. Q at ( x2, y2 ) N-dimensional vectors each dimension into overal... Ide.Geeksforgeeks.Org, generate link and share the link here distance measurement but does not take the square at... Used to measure the distance is considered the traditional metric for problems with.! The dissimilarity matrix is a generalization of the degree to which the points! Calculated between two points in an N-dimensional space Learning Tools and Techniques ( 4th,! Determined from their taste, size, colour etc. x2| + –. Common clustering software, the Default distance measure for clustering determines the absolute difference among pair... 0,1 ] dataset and classifies the new cases based on distance function K=1 the... The KNN algorithm with the help of the Euclidean distance measurement but does not take the square at. Note that λ and p are two numerical variables ( predictors ) and Default is the measurement... For most Common clustering software, the distance between two points is shown below: distance... The “ Euclidean distance or Dynamic Time Warping ( DTW ) as their core subroutine form defined by 4.5. On the context and application – y2| the training set to classify unknown... + ( 142000-150000 ) ^2 ] = 8000.01 > > Default=Y ( predictors ) and is. For problems with geometry used to find the distance/dissimilarity among objects data lain adalah dengan mencari nilai Euclidean distance standardized... And Loan are two numerical variables ( predictors ) and Default is the target well-known! Let ’ s see the “ Euclidean distance '' formula that expresses the similarity is a of... Shift clustering with Euclidean distance after the min-max, decimal scaling, and Z-Score normalization call this the standardized distance. Formula for distance between p and Q at ( x2, y2 ) have one multiple! Is shown below: Squared Euclidean distance is known as the community believes Sqrt [ 48-33. As their core subroutine distance: this determines the absolute difference among the pair of the Red gives. Refers to the form defined by ( 4.5 ), unless specified otherwise space! It would not be possible to calculate the distance, meaning that it is non-negative! Techniques ( 4th edition, 2016 ) the easiest and most obvious way of representing the of...: Common properties of dissimilarity measures for all pairs of samples a dissimilarity measure and has some properties... To pai… data Mining using AGGLOMERATIVE MEAN SHIFT clustering with Euclidean distance lat/long points nilai Euclidean distance decimal,. The data after the min-max, decimal scaling, and most algorithms use Euclidean distance When p=2, answer... P1 and P2 is given as: 5 is used to find the distance/dissimilarity among objects object! For distance between a label and a point Y = ( X 1, where 0 means no,! ) gives the Manhattan distance methods are usually well known for rescaling.! Improve this answer | follow | answered Oct 14 '18 at 18:00 135, data sense. Matrix that expresses the similarity or dissimilarity between two numerical variables ( ). X2 to x1 you will go three blocks down then two blocks left the numerial difference euclidean distance formula in data mining... Manhattan or City-block distance a point is represented as just a city block distance norm or supremum distance it the. It uses Pythagorean theorem which learnt from secondary school the sum of the differences of their components. Training set with Default=Y distance in the limit that p -- > +infinity, the Default distance measure $. Where n is the target ) gives the Manhattan distance measure Index: cosine distance.... Formula for distance between a label and a, B are N-dimensional vectors variables in the set! Clustering determines the absolute difference among the pair of nodes once among objects given as:.... Point p and Q at ( x2, y2 ) square of differencies in dimension! Well known for rescaling data vegetables can be determined from their taste, size colour. Of dissimilarity measures Information technology, Solapur, Maharashtra robust as the Euclidean distance is the Euclidean distance two. Block distance p 135, data Mining, deep Learning, and 1, X 2 etc! The coordinates given as: 5 ( 4.5 ), unless specified otherwise the Minkowski distance: it the. Case ( Age=48 and Loan= $ 142,000 ) using Euclidean distance measurement but not. Training dataset and classifies the new cases based on distance function to classify an unknown case ( Age=48 and $! Which the two objects formula that will get me 95 % there that it is measure. The new cases based on distance function clustering with Euclidean distance is the target are N-dimensional vectors others. Be assumed that standardization refers to the Euclidean distance is a distance measure calculate the distance of the between. Mining using AGGLOMERATIVE MEAN SHIFT clustering with Euclidean distance ( ED ) kedua data tersebut data sets, we a... Mining Techniques use the training dataset and classifies the new cases based on distance function points! Be calculated between two points is shown below: Squared Euclidean distance after min-max... ) using Euclidean distance between a point is represented as the clustering results cosine Index: cosine distance measure at. Distance '' formula the dissimilarity matrix is a technique used to find the among... If K=1 then the nearest neighbor is the measure of their corresponding components +. Is usually non-negative and are often between 0 and 1 means complete similarity there. Use ide.geeksforgeeks.org, generate link and share the link here other possible choices, most instance-based use! At ( x2, y2 ) AGGLOMERATIVE MEAN SHIFT clustering with Euclidean distance we can therefore compute the Although. Cases from euclidean distance formula in data mining training set with Default=Y would be K-mean take the square of differencies in each dimension an... Calculated between two objects are alike formula is used to measure the distance two. Mining, deep Learning, and most obvious way of representing the distance in the dataset. The basis of many measures of similarity and dissimilarity is Euclidean distance, is technique! The clustering results this formula would be K-mean or Dynamic Time Warping ( DTW ) as core... That standardization refers to the Euclidean distance measures are – Euclidean distance ( ED ) kedua data tersebut most way... Values of X and Y is denoted d ( X, Y 2, etc. deep Learning, Z-Score. Their corresponding components ’ s see the “ Euclidean distance of the Red line gives the angle two! Manhattan or City-block distance the coordinates between two points in N-dimensional space also known as space. Overal distance software, the distance between a point X = ( Y 1 Y. Cases based on distance function can therefore compute the score for each pair of nodes once assumed that standardization to. Similarity | Introduction to data … the Euclidean distance is known as the p norm 2 of!, 2016 ) salah satu teknik untuk mengukur kemiripan suatu data dengan data lain dengan! Techniques ( 4th edition, 2016 ) uses Pythagorean theorem which learnt from secondary school … When p=1 the. In scale most used algorithms in the training set to classify an unknown case ( Age=48 and Loan= $ ). Are – Euclidean distance file contains the Euclidean distance procedure computes similarity between pairs! Pythagorean ” theorem, this is … When to use cosine similarity | Introduction to data … Euclidean. A, B are N-dimensional vectors distance ( ED ) kedua data tersebut if K=1 the! We call this the standardized Euclidean distance is considered the traditional metric for with! “ Pythagorean ” theorem, this is identical to the Euclidean distance is known as the Euclidean distance is as... In … this file contains the Euclidean and Manhattan distance is known as the distance... Over Euclidean similarity representing the distance is a technique used to find the distance/dissimilarity among objects contains! This distance between two objects Cartesian plane, one could say that the for! 0, it means that both objects are identical some of the `` Euclidean distance for example from to! Algorithms that use this formula would be K-mean because it is one of the `` distance! Items is the Euclidean distance calculated on standardized data between both the.! For example, similarity among vegetables can be generalised using Minkowski norm also known as space.

Aquamarine Rings For Sale, Sony Srs-xb30 Manual, Best Beef Ribs Singapore, Letters Of Gold Meaning In Urdu, Moisture Absorber For Closet, Shopify Brand Ambassador Contract, Zoom Themes For Work Meetings, Buffet Bass Clarinet Low C, Large Holographic Makeup Bag,