Buat elipsnya

Area yang dibatasi oleh elips merepresentasikan 95% observasi dalam klaster, sehingga kurva ini berguna untuk memperlihatkan posisi klaster. Karena kita sudah mengekstraksi nilai mean (mean_comp_number) dan matriks kovarians (covariance_comp_number), kita dapat dengan mudah membuat elips untuk dua klaster tersebut.

Perlu diingat bahwa elips tidak mempertimbangkan proporsi dan hanya menunjukkan lokasi klaster, bukan tingkat kepentingannya.

Latihan ini adalah bagian dari kursus

Model Campuran di R

Petunjuk latihan

Gunakan fungsi ellipse() untuk membuat kurva ini. Fungsi tersebut menggunakan matriks kovarians sebagai argumen x dan mean sebagai argumen centre.
Gunakan jumlah baris dalam himpunan data gender sebagai npoints.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Create ellipse curve 1
ellipse_comp_1 <- ___(x = ___, 
                          centre = ___,
                          npoints = nrow(___))
head(ellipse_comp_1)

# Create ellipse curve 2
ellipse_comp_2 <- ___(x = ___, 
                          centre = ___,
                          npoints = ___(gender))
head(ellipse_comp_2)

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Model Campuran di R

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus Gratis

In this chapter, you will be introduced to fundamental concepts in model-based clustering and how this approach differs from other clustering techniques. You will learn the generating process of Gaussian Mixture Models as well as how to visualize the clusters.

Exercise 1: Introduction to model-based clustering Exercise 2: Clustering approaches Exercise 3: Explore gender data Exercise 4: Gaussian distribution Exercise 5: Sampling a Gaussian distribution Exercise 6: (not so good) Estimations of the mean and sd Exercise 7: Gaussian mixture models (GMM)Exercise 8: Simulate a mixture of two Gaussian distributions Exercise 9: Plot histogram of Gaussian Mixture Exercise 10: Mixture of three Gaussian distributions

In this chapter, you will be introduced to the main structure of Mixture Models, how to address different data with this approach and how to estimate the parameters involved. To accomplish the estimation, you will learn an iterative method called Expectation-Maximization algorithm.

Exercise 1: Structure of mixture models Exercise 2: Which probability distribution?Exercise 3: Handwritten digits dataset Exercise 4: Parameters estimation Exercise 5: Estimation given the probabilities Exercise 6: Calculating the probabilities Exercise 7: EM algorithm Exercise 8: Expectation function Exercise 9: Maximization function Exercise 10: Apply the two steps Exercise 11: Plot the estimated clusters

This chapter shows how to fit Gaussian Mixture Models in 1 and 2 dimensions with `flexmix` package. The data used is formed by 10.000 observations of people with their weight, height, body mass index and informed gender.

Exercise 1: Model Campuran Gaussian Univariat Exercise 2: Jumlah klaster Exercise 3: Jumlah parameter Exercise 4: Model Campuran Gaussian Univariat dengan flexmix Exercise 5: Kasus univariat dengan flexmix Exercise 6: Mengambil Parameter untuk Kasus Univariat Exercise 7: Memvisualisasikan Gaussian Mixture Model Univariat Exercise 8: Bandingkan hasilnya Exercise 9: Model Campuran Gaussian Bivariat Exercise 10: Cross-term dari matriks kovarians Exercise 11: Parameter pada kasus bivariat Exercise 12: Model Campuran Gaussian Bivariat dengan flexmix Exercise 13: Sesuaikan model dengan suku silang Exercise 14: Ambil komponennya Exercise 15: Buat elipsnya

Latihan Saat Ini

Exercise 16: Visualisasikan klaster

In this module, you will learn how Mixture Models extends to consider probability distributions different from the Gaussian and how these models are fitted with `flexmix`. The datasets used are handwritten digits images and the number of crimes in Chicago city. For the first dataset you will find clusters that summarize the handwritten digits and for the second dataset, you will find clusters of communities where is more or less dangerous to live in.

Exercise 1: Bernoulli Mixture Models Exercise 2: Binary images Exercise 3: How many values?Exercise 4: Bernoulli Mixture Models with flexmix Exercise 5: Handwritten digits with `flexmix`Exercise 6: Poisson Mixture Models Exercise 7: Discover the lambda Exercise 8: Sample from Poisson distribution Exercise 9: Poisson Mixture Models with flexmix Exercise 10: Crimes data with `flexmix`