Implementasi Clustering pada Dataset Customer Segmentation

Load & Preparation

# Read Dataset
df_raw <- read.csv("Cust_Segmentation.csv")

# Data Cleaning

# 1. drop kolom pertama (ID)
df <- df_raw %>% dplyr::select(-1)

# 2. hanya ambil variabel numerik
df <- df %>% dplyr::select(where(is.numeric))

# 3. hapus baris dengan NA agar tidak error di FCM atau Mean Shift
df <- na.omit(df)

# 4. cek missing value
missing_table <- data.frame(Variabel = colnames(df), Jumlah_NA = colSums(is.na(df)))
missing_table %>% kable(caption = "Tabel 1: Hasil Identifikasi Missing Value") %>% kable_styling(bootstrap_options = c("striped","hover","condensed"), full_width = F)

Tabel 1: Hasil Identifikasi Missing Value

	Variabel	Jumlah_NA
Age	Age	0
Edu	Edu	0
Years.Employed	Years.Employed	0
Income	Income	0
Card.Debt	Card.Debt	0
Other.Debt	Other.Debt	0
Defaulted	Defaulted	0
DebtIncomeRatio	DebtIncomeRatio	0

# 5. cek data duplikasi
data.frame(Keterangan = "Total Baris Duplikat", Jumlah = sum(duplicated(df))) %>% kable(caption = "Tabel 2: Hasil Identifikasi Duplikasi Data") %>% kable_styling(bootstrap_options = c("striped","hover"), full_width = F)

Tabel 2: Hasil Identifikasi Duplikasi Data

Keterangan	Jumlah
Total Baris Duplikat	0

# 6. cek outlier
df %>%
  pivot_longer(cols = everything()) %>%
  ggplot(aes(x = name, y = value, fill = name)) +
  geom_boxplot() +
  facet_wrap(~name, scales = "free") +
  theme_minimal() +
  theme(legend.position = "none") +
  labs(title = "Identifikasi Outlier melalui Boxplot")

# 7. statistik deskriptif
stat_deskriptif <- describe(df)[, c("n","mean","sd","min","max")]
stat_deskriptif %>% kable(caption = "Tabel 3: Ringkasan Statistik Deskriptif Data", digits = 2) %>% kable_styling(bootstrap_options = c("striped","hover"), full_width = T)

Tabel 3: Ringkasan Statistik Deskriptif Data

	n	mean	sd	min	max
Age	700	34.86	8.00	20.00	56.00
Edu	700	1.72	0.93	1.00	5.00
Years.Employed	700	8.39	6.66	0.00	31.00
Income	700	45.60	36.81	14.00	446.00
Card.Debt	700	1.55	2.12	0.01	20.56
Other.Debt	700	3.06	3.29	0.05	27.03
Defaulted	700	0.26	0.44	0.00	1.00
DebtIncomeRatio	700	10.26	6.83	0.40	41.30

# 8. Scaling Data
df_scaled <- scale(df)

# Menghapus kolom yang menjadi NaN jika ada varians 0
df_scaled <- df_scaled[, colSums(is.na(df_scaled)) == 0]

Menampilkan Dataset

Dataset Customer Segmentation

	Age	Edu	Years.Employed	Income	Card.Debt	Other.Debt	Defaulted	DebtIncomeRatio
1	41	2	6	19	0.124	1.073	0	6.3
2	47	1	26	100	4.582	8.218	0	12.8
3	33	2	10	57	6.111	5.802	1	20.9
4	29	2	4	19	0.681	0.516	0	6.3
5	47	1	31	253	9.308	8.908	0	7.2
7	38	2	4	56	0.442	0.454	0	1.6
8	42	3	0	64	0.279	3.945	0	6.6
10	47	3	23	115	0.653	3.947	0	4.0
11	44	3	8	88	0.285	5.083	1	6.1
13	24	1	7	18	0.526	0.643	0	6.5
15	28	3	2	20	0.233	1.647	1	9.4
17	29	1	1	17	0.132	0.293	0	2.5
18	43	4	1	26	1.519	1.237	0	10.6
21	29	2	6	25	0.585	0.465	0	4.2
22	36	3	10	43	0.961	4.629	0	13.0
23	28	3	6	47	5.574	3.732	1	19.8
24	45	1	19	77	2.303	4.165	0	8.4
25	37	4	10	123	3.022	18.257	0	17.3
27	24	1	4	21	0.099	0.447	0	2.6
29	38	2	13	59	0.408	1.008	0	2.4
30	34	2	9	45	1.118	3.427	0	10.1
31	40	3	18	100	1.485	1.015	0	2.5
32	42	2	12	55	2.533	5.717	0	15.0
33	23	2	0	42	1.019	0.619	1	3.9
35	28	1	12	45	1.854	1.611	0	7.7
37	35	3	5	37	0.581	1.417	1	5.4
38	37	1	0	18	1.584	0.738	1	12.9
39	22	1	1	18	0.270	0.522	0	4.4
41	20	1	4	14	0.201	1.157	1	9.7
42	48	3	17	113	3.376	10.184	0	12.0
43	28	2	5	34	3.099	4.993	0	23.8
44	37	5	9	177	0.888	9.555	0	5.9
45	48	1	3	27	1.403	4.348	0	21.3
47	22	1	4	14	0.225	2.225	0	17.5
49	28	1	3	19	0.261	0.518	0	4.1
51	47	1	22	81	1.506	2.949	0	5.5
52	36	1	11	33	1.266	9.459	0	32.5
53	24	1	3	19	1.358	3.278	1	24.4
55	29	3	5	70	3.176	10.754	1	19.9
56	34	1	2	25	0.573	2.577	1	12.6
57	32	1	1	20	0.315	0.645	1	4.8
58	27	3	2	26	0.722	0.838	0	6.0
59	40	1	9	64	0.790	3.434	0	6.6
60	50	1	11	36	0.835	0.893	0	4.8
61	39	1	17	60	2.450	5.770	0	13.7
62	34	1	18	34	2.100	3.136	0	15.4
63	36	1	18	67	5.245	2.259	0	11.2
64	44	2	18	74	4.522	5.394	0	13.4
65	24	2	4	20	0.324	0.416	0	3.7
66	31	1	8	44	1.721	1.799	0	8.0
67	34	1	16	79	1.735	1.425	0	4.0
68	26	1	4	27	2.472	0.363	1	10.5
69	38	1	3	23	0.832	2.549	0	14.7
70	24	1	3	14	0.838	1.416	1	16.1
71	30	3	8	61	0.550	1.158	0	2.8
72	40	4	5	75	0.885	0.540	0	1.9
73	30	2	12	98	2.935	4.121	0	7.2
75	29	1	9	33	0.694	0.890	0	4.8
76	33	1	5	18	0.985	1.337	0	12.9
77	43	1	8	45	0.677	0.808	0	3.3
78	35	2	1	21	0.370	1.289	0	7.9
79	41	2	21	145	3.237	14.453	0	12.2
81	33	1	12	68	1.366	5.978	0	10.8
82	30	3	0	65	3.900	15.405	1	29.7
83	40	3	18	157	3.326	7.036	0	6.6
85	49	2	11	51	0.543	7.107	0	15.0
89	28	2	8	31	1.492	1.050	1	8.2
90	23	4	0	32	0.818	5.070	1	18.4
91	37	2	11	75	0.633	1.992	0	3.5
92	35	2	5	33	0.103	0.821	0	2.8
93	31	1	1	21	0.277	0.248	1	2.5
94	29	1	1	20	0.569	3.151	1	18.6
95	34	3	7	27	0.125	0.469	0	2.2
98	38	1	10	34	0.036	0.372	0	1.2
99	32	2	5	28	2.594	1.550	1	14.8
100	22	3	0	20	0.219	0.721	0	4.7
101	30	1	7	33	1.165	7.217	1	25.4
103	36	1	20	60	1.056	2.184	0	5.4
104	44	1	5	48	1.970	2.350	1	9.0
105	27	2	4	22	1.086	1.818	1	13.2
106	47	1	19	50	3.176	11.874	0	30.1
107	41	1	7	32	0.072	0.856	0	2.9
108	25	1	8	27	1.019	2.869	0	14.4
110	42	3	13	82	1.315	1.391	0	3.3
111	36	1	14	53	1.888	1.133	0	5.7
112	25	4	0	32	2.140	3.492	0	17.6
113	30	1	10	39	2.056	1.649	0	9.5
114	24	1	0	16	1.009	1.551	1	16.0
116	39	2	9	32	1.430	0.682	0	6.6
117	30	1	4	18	0.227	1.699	0	10.7
119	32	2	12	63	5.550	5.916	0	18.2
120	33	1	7	26	0.543	1.017	0	6.0
121	29	2	6	21	0.369	2.046	0	11.5
122	28	2	5	37	1.124	1.947	0	8.3
123	33	4	9	32	0.496	1.264	0	5.5
124	39	2	16	53	0.694	1.267	0	3.7
125	32	1	10	42	1.540	2.282	0	9.1
127	32	2	3	47	0.429	0.464	0	1.9
128	28	2	5	25	0.339	0.386	0	2.9
129	26	1	2	21	1.023	3.135	0	19.8
130	27	1	0	16	0.183	0.089	0	1.7
131	35	1	16	36	0.179	1.045	0	3.4
132	35	1	16	57	1.143	4.842	0	10.5
133	31	2	6	25	2.332	0.543	0	11.5
134	32	3	5	23	0.326	0.709	0	4.5
135	29	1	9	25	1.070	2.405	0	13.9
136	35	4	4	29	1.844	1.346	0	11.0
139	44	1	12	31	0.358	2.649	0	9.7
140	40	2	5	35	1.584	4.156	1	16.4
141	36	2	13	41	2.918	3.806	1	16.4
142	23	2	0	21	0.455	1.372	0	8.7
143	26	2	8	40	0.444	4.276	0	11.8
144	30	1	8	23	0.762	1.998	0	12.0
145	34	1	12	68	7.817	9.251	1	25.1
146	28	3	2	30	1.522	4.448	0	19.9
148	46	3	3	43	3.042	2.634	1	13.2
149	24	2	1	42	0.838	1.556	0	5.7
150	25	2	6	26	0.070	1.100	0	4.5
151	33	1	14	37	0.199	0.763	0	2.6
153	38	3	4	31	1.332	1.551	1	9.3
155	34	2	9	65	2.108	1.922	0	6.2
157	39	1	22	113	0.987	1.951	0	2.6
158	44	1	24	101	2.992	2.664	0	5.6
159	28	2	1	24	1.338	2.766	1	17.1
160	31	3	9	59	3.110	5.504	1	14.6
162	36	3	13	39	2.801	4.687	0	19.2
163	35	2	14	82	0.468	0.188	0	0.8
164	31	1	11	47	2.864	2.259	0	10.9
165	28	2	6	22	0.650	1.880	0	11.5
166	36	4	8	32	0.025	0.487	0	1.6
167	31	3	6	54	0.402	1.110	0	2.8
168	30	1	11	27	0.074	0.169	0	0.9
169	26	1	10	24	0.311	0.337	0	2.7
170	48	4	3	45	0.975	3.435	0	9.8
171	39	2	16	89	5.002	7.725	0	14.3
172	31	1	7	20	0.111	2.349	0	12.3
173	31	3	9	28	0.122	0.606	0	2.6
174	27	3	3	45	0.292	4.658	1	11.0
175	31	1	13	27	0.238	1.112	0	5.0
176	53	1	0	27	2.754	5.049	1	28.9
177	40	1	15	55	0.856	2.169	0	5.5
178	26	1	7	22	0.209	0.913	0	5.1
179	29	2	5	36	3.491	0.973	1	12.4
180	52	1	19	89	0.397	3.786	0	4.7
181	39	2	2	46	4.004	3.356	1	16.0
182	33	2	13	40	0.181	2.699	0	7.2
183	24	1	7	21	0.520	0.803	0	6.3
184	47	1	29	129	20.561	12.076	1	25.3
185	41	3	2	15	1.961	1.984	1	26.3
186	26	2	6	45	6.049	5.651	0	26.0
187	48	1	3	24	0.764	0.916	0	7.0
188	37	1	13	24	0.602	1.534	0	8.9
192	47	1	15	30	2.212	5.138	0	24.5
194	37	2	0	31	0.118	0.161	1	0.9
195	40	2	15	73	1.033	1.449	0	3.4
197	21	2	0	16	0.150	0.938	0	6.8
198	52	1	24	64	3.930	2.470	0	10.0
199	47	3	16	221	15.792	23.104	1	17.6
200	41	3	0	26	0.099	0.343	0	1.7
201	47	1	27	113	2.381	8.015	0	9.2
203	22	3	1	25	1.977	1.473	1	13.8
204	39	1	8	21	0.276	0.564	0	4.0
206	51	1	10	44	4.960	0.848	1	13.2
207	33	2	10	26	0.031	0.775	0	3.1
208	43	1	25	242	1.636	4.656	0	2.6
209	40	1	22	95	0.633	2.787	0	3.6
210	27	4	2	23	0.320	0.991	1	5.7
211	31	1	11	25	0.141	2.134	0	9.1
213	37	1	9	57	1.357	1.778	0	5.5
215	42	1	3	24	0.667	1.877	0	10.6
216	25	1	3	16	1.011	1.837	1	17.8
217	39	2	7	68	1.017	7.823	1	13.0
220	48	1	15	60	0.321	0.939	0	2.1
221	21	2	0	21	0.488	2.137	1	12.5
222	40	3	17	116	2.561	4.399	0	6.0
223	31	2	9	30	0.602	1.138	0	5.8
224	23	4	0	23	0.468	1.073	0	6.7
225	31	1	6	21	0.729	1.014	0	8.3
226	31	1	7	23	0.225	0.902	0	4.9
227	50	3	18	102	5.782	8.600	0	14.1
228	29	1	1	31	0.156	2.324	0	8.0
229	37	3	5	29	1.405	5.729	0	24.6
230	46	1	16	52	3.032	3.676	1	12.9
231	39	1	9	35	0.233	1.517	0	5.0
232	34	1	14	28	1.138	3.622	0	17.0
233	27	1	11	56	4.637	4.827	0	16.9
235	41	1	24	83	6.589	5.031	0	14.0
236	29	1	1	18	0.030	0.168	0	1.1
238	24	2	0	15	0.321	2.094	1	16.1
239	32	3	9	51	2.686	9.044	0	23.0
240	23	2	3	34	0.262	1.234	0	4.4
241	31	1	4	28	0.291	2.873	0	11.3
242	36	1	15	39	0.686	0.562	0	3.2
244	26	1	10	32	0.126	1.378	0	4.7
247	43	1	10	69	0.411	4.005	0	6.4
248	37	1	18	54	4.593	1.941	0	12.1
250	28	4	1	26	0.377	2.847	0	12.4
251	28	1	8	30	0.613	1.547	0	7.2
252	26	3	2	37	0.205	5.049	0	14.2
253	39	1	10	31	0.185	1.303	0	4.8
254	34	2	3	21	1.402	3.449	0	23.1
255	51	3	16	82	2.605	1.003	0	4.4
256	28	2	3	41	5.897	4.394	1	25.1
257	39	1	20	39	0.774	2.424	0	8.2
258	23	1	3	19	0.279	1.317	1	8.4
260	21	1	4	26	1.421	0.893	0	8.9
261	40	1	3	23	0.409	1.178	0	6.9
263	32	2	12	54	3.196	4.580	0	14.4
264	29	3	3	50	0.259	0.941	1	2.4
265	43	1	25	64	0.951	9.737	0	16.7
266	55	1	3	40	0.563	2.637	1	8.0
268	38	3	15	63	5.091	3.351	0	13.4
269	40	1	2	28	0.185	1.803	0	7.1
270	43	1	12	38	0.129	1.239	0	3.6
271	35	2	1	24	0.591	2.409	1	12.5
272	24	1	8	24	0.382	0.818	0	5.0
275	41	2	5	25	0.393	2.157	0	10.2
276	28	1	2	17	0.199	0.226	1	2.5
277	24	2	2	28	1.787	3.057	1	17.3
278	40	3	3	55	1.751	7.324	1	16.5
279	29	1	8	27	0.402	2.244	0	9.8
280	26	2	4	28	0.226	2.434	0	9.5
283	41	1	15	120	2.659	0.821	0	2.9
284	26	1	0	14	0.256	1.214	0	10.5
285	48	1	22	100	3.704	5.396	0	9.1
287	35	1	15	77	5.501	2.661	0	10.6
288	23	1	7	31	0.338	1.708	0	6.6
289	39	2	20	101	0.341	1.073	0	1.4
290	38	1	21	58	4.584	4.986	0	16.5
291	30	1	4	33	0.500	0.886	0	4.2
293	33	1	0	23	0.077	0.130	0	0.9
294	34	1	15	39	4.373	5.260	1	24.7
295	27	2	6	34	0.238	1.258	0	4.4
296	30	1	1	22	1.139	1.171	0	10.5
297	27	2	1	20	0.377	1.683	1	10.3
298	29	1	9	30	0.155	2.305	0	8.2
299	25	4	1	34	0.133	2.757	1	8.5
300	41	1	23	91	0.573	11.894	0	13.7
301	42	2	11	73	2.869	2.095	0	6.8
302	38	3	12	64	0.895	2.817	0	5.8
303	23	1	5	17	0.302	1.041	0	7.9
304	33	1	2	24	1.041	1.263	0	9.6
305	31	1	2	22	1.739	1.869	1	16.4
306	37	2	6	62	1.186	4.084	0	8.5
307	40	1	10	45	2.013	6.447	0	18.8
309	35	2	3	40	1.803	5.077	1	17.2
310	38	1	6	41	0.266	3.383	0	8.9
311	30	2	8	56	0.333	3.251	0	6.4
312	30	1	1	17	0.746	1.124	0	11.0
314	29	2	3	15	0.147	0.243	0	2.6
315	39	1	19	45	0.916	0.749	0	3.7
318	31	2	0	27	1.089	2.367	1	12.8
320	38	1	0	23	0.898	0.965	1	8.1
321	41	3	13	91	2.316	20.616	1	25.2
322	41	2	2	30	0.113	1.897	0	6.7
323	45	3	5	94	3.612	6.258	1	10.5
325	22	1	4	16	0.084	0.108	0	1.2
326	34	1	13	56	0.864	2.552	0	6.1
327	25	1	0	18	0.514	2.888	1	18.9
328	29	2	6	18	0.450	0.468	1	5.1
329	35	2	13	105	1.418	0.577	0	1.9
330	39	1	19	48	1.930	4.358	0	13.1
331	34	1	17	59	1.808	2.912	0	8.0
332	44	2	21	113	3.192	11.724	0	13.2
333	23	2	0	21	0.776	1.618	1	11.4
334	46	1	6	31	1.986	1.796	0	12.2
335	41	4	9	47	0.437	1.913	0	5.0
336	52	1	9	24	0.802	3.806	1	19.2
337	23	1	1	17	0.012	0.260	0	1.6
338	48	1	28	70	4.334	4.066	0	12.0
340	53	2	16	44	1.333	1.131	0	5.6
341	39	3	11	39	2.561	2.470	0	12.9
342	37	1	20	56	0.543	0.521	0	1.9
344	49	2	2	20	0.379	3.001	1	16.9
345	39	1	23	75	3.133	11.042	0	18.9
346	42	2	7	41	0.941	0.945	0	4.6
347	28	2	1	16	0.574	1.778	0	14.7
348	27	3	5	75	2.207	2.518	0	6.3
349	24	1	2	21	0.025	0.101	0	0.6
350	34	1	5	33	0.778	1.631	0	7.3
351	47	1	31	136	14.231	17.185	1	23.1
352	26	4	1	27	0.312	0.471	0	2.9
353	35	1	17	42	0.093	1.167	0	3.0
354	31	1	6	33	0.146	1.240	0	4.2
355	29	1	13	27	0.069	0.552	0	2.3
356	44	1	9	33	0.546	1.170	0	5.2
357	40	2	13	102	6.227	13.051	1	18.9
358	36	4	1	30	0.324	3.126	0	11.5
360	21	1	1	18	0.159	2.955	0	17.3
361	42	4	15	186	2.404	4.292	0	3.6
362	37	2	4	20	0.174	4.166	1	21.7
363	24	1	6	21	0.108	0.984	0	5.2
364	24	4	0	29	0.590	1.324	1	6.6
365	46	3	4	73	2.223	1.719	0	5.4
366	28	1	2	16	0.871	1.561	0	15.2
367	41	1	16	68	0.448	3.224	0	5.4
368	46	5	15	126	0.477	3.429	0	3.1
369	51	2	22	120	4.140	4.980	0	7.6
370	36	1	17	54	0.727	1.649	0	4.4
371	25	1	1	20	0.472	0.868	1	6.7
372	27	2	3	33	1.633	2.723	0	13.2
373	43	1	23	72	1.182	4.290	0	7.6
374	53	1	6	27	0.594	1.134	1	6.4
375	32	1	4	16	0.894	3.666	1	28.5
376	36	1	0	25	2.778	2.147	0	19.7
378	35	1	18	42	0.205	2.903	0	7.4
379	32	2	0	18	0.296	0.748	1	5.8
381	42	1	2	30	1.472	1.438	0	9.7
382	25	1	9	20	0.105	0.595	0	3.5
383	20	3	0	17	0.044	0.347	0	2.3
384	50	1	4	23	0.141	2.734	0	12.5
385	46	2	22	144	3.325	12.659	0	11.1
386	33	1	15	32	0.491	1.941	0	7.6
387	43	1	19	79	0.782	5.143	0	7.5
388	30	2	4	21	0.492	3.351	0	18.3
389	34	1	3	36	0.624	1.068	0	4.7
390	37	1	3	25	0.276	0.649	0	3.7
391	38	1	4	19	1.245	1.339	0	13.6
392	32	2	11	57	5.440	8.126	1	23.8
393	39	1	15	39	0.140	0.562	0	1.8
394	31	1	7	41	2.996	9.591	1	30.7
395	25	1	5	22	0.403	1.995	0	10.9
398	45	2	21	132	2.558	1.402	0	3.0
399	39	1	4	38	1.178	1.292	0	6.5
401	24	2	3	24	2.173	2.411	0	19.1
402	30	1	10	19	0.336	0.500	0	4.4
403	30	1	0	19	0.624	2.530	1	16.6
404	27	1	0	18	0.583	1.721	0	12.8
405	31	1	5	25	1.177	2.348	0	14.1
406	47	3	9	52	2.540	7.912	1	20.1
407	47	2	4	33	1.195	1.148	1	7.1
408	45	2	15	51	1.551	7.680	0	18.1
411	29	1	12	40	1.632	1.048	0	6.7
412	36	2	12	60	1.721	3.019	0	7.9
413	29	2	10	44	3.703	4.261	0	18.1
414	39	1	2	22	1.916	3.166	1	23.1
415	22	2	0	35	0.781	2.369	1	9.0
416	46	1	14	35	1.349	2.221	0	10.2
418	37	1	1	24	1.801	1.823	1	15.1
419	42	1	12	51	2.412	8.502	0	21.4
421	34	1	18	53	0.840	4.725	0	10.5
422	38	3	12	63	5.715	4.365	1	16.0
424	35	2	9	30	0.650	1.630	0	7.6
426	45	2	18	35	0.548	5.542	0	17.4
427	43	1	4	29	0.053	1.281	0	4.6
428	54	1	9	28	1.558	2.586	1	14.8
429	35	3	7	38	1.383	4.355	0	15.1
430	28	1	4	19	0.287	0.492	0	4.1
431	39	1	16	52	4.160	6.240	0	20.0
433	32	2	2	15	0.119	1.246	1	9.1
434	22	3	0	18	0.478	0.908	0	7.7
435	41	3	18	71	6.566	4.581	1	15.7
436	34	2	4	25	0.570	0.880	0	5.8
437	34	2	9	27	0.295	0.164	0	1.7
438	31	1	1	24	0.217	0.863	0	4.5
439	55	1	19	78	4.405	7.763	0	15.6
440	40	1	21	46	0.967	1.885	0	6.2
441	41	1	13	44	0.071	0.677	0	1.7
442	43	2	10	37	0.676	2.469	0	8.5
443	36	1	6	26	0.030	2.128	0	8.3
444	51	2	31	249	4.273	15.149	0	7.8
446	27	3	5	42	2.708	5.650	1	19.9
447	36	2	11	49	0.273	0.364	0	1.3
448	38	1	5	54	1.500	4.224	0	10.6
449	47	1	17	43	0.588	1.820	0	5.6
450	33	1	13	42	0.228	1.284	0	3.6
451	41	3	12	86	4.991	17.799	1	26.5
452	33	1	9	29	1.349	2.653	1	13.8
453	21	3	0	26	1.530	0.472	0	7.7
454	34	1	0	21	0.187	1.325	0	7.2
455	51	2	27	166	0.821	18.269	0	11.5
456	36	2	0	30	0.329	2.581	0	9.7
457	23	2	0	17	2.044	2.665	1	27.7
459	29	1	7	22	0.551	1.583	0	9.7
460	46	3	9	60	1.607	7.574	0	15.3
461	45	3	2	39	0.459	1.452	0	4.9
462	41	3	17	176	11.359	5.009	1	9.3
463	28	1	4	16	1.085	2.723	0	23.8
464	37	1	6	29	1.716	3.011	0	16.3
465	25	1	8	35	0.077	0.938	0	2.9
466	48	3	0	19	0.850	1.202	0	10.8
467	37	3	12	83	0.320	3.083	0	4.1
468	32	1	3	23	0.053	1.373	0	6.2
469	31	1	15	60	0.804	2.076	0	4.8
470	48	4	14	59	4.936	4.799	1	16.5
471	45	1	22	91	1.256	9.391	0	11.7
473	39	1	19	46	2.516	1.670	0	9.1
474	41	1	19	96	2.254	5.234	0	7.8
475	50	2	15	60	0.468	0.192	0	1.1
476	22	3	0	23	0.373	1.559	1	8.4
477	43	2	6	54	0.622	4.562	0	9.6
478	33	1	8	27	0.777	0.789	0	5.8
479	29	1	11	32	0.927	0.993	0	6.0
480	24	1	3	21	2.682	3.387	1	28.9
482	37	4	1	33	2.694	2.619	1	16.1
483	34	2	8	78	2.157	2.055	0	5.4
484	22	1	1	17	0.806	2.356	0	18.6
485	41	1	4	25	1.060	1.464	0	10.1
486	32	2	7	32	0.433	0.943	0	4.3
487	35	1	15	40	1.214	6.186	0	18.5
488	41	1	24	100	5.060	6.440	0	11.5
489	21	2	1	16	0.242	2.638	1	18.0
490	23	1	2	25	0.124	1.001	1	4.5
491	25	1	6	22	2.262	2.182	0	20.2
492	40	1	12	89	2.772	12.714	0	17.4
493	38	1	11	35	1.266	1.534	0	8.0
495	40	1	13	28	0.965	0.687	0	5.9
496	29	2	1	25	0.569	2.031	0	10.4
497	25	1	3	18	0.352	2.582	1	16.3
498	33	1	14	72	15.017	14.719	1	41.3
500	24	1	1	18	0.239	0.823	1	5.9
501	29	2	4	37	1.160	1.282	1	6.6
502	28	2	0	29	2.148	1.709	1	13.3
505	31	2	2	26	0.283	1.485	1	6.8
506	28	3	5	44	0.773	3.055	0	8.7
507	39	2	15	32	0.422	1.146	0	4.9
508	33	2	2	35	2.124	5.891	1	22.9
509	41	2	19	68	1.507	2.437	0	5.8
510	29	2	1	29	2.330	3.818	1	21.2
511	31	1	2	22	1.092	1.702	1	12.7
512	28	4	0	38	0.636	3.316	0	10.4
513	35	1	0	34	1.370	2.404	1	11.1
514	54	1	25	120	14.596	17.204	1	26.5
516	46	1	9	31	1.617	2.661	0	13.8
517	23	2	3	22	1.274	1.234	1	11.4
518	28	2	6	38	0.774	1.962	0	7.2
519	24	1	3	15	0.073	1.622	1	11.3
520	41	2	7	63	4.638	5.064	0	15.4
521	43	2	9	60	4.039	8.201	1	20.4
522	27	3	3	35	1.601	3.054	0	13.3
524	28	1	2	18	1.939	0.725	1	14.8
526	35	3	1	20	0.853	0.727	0	7.9
527	31	1	11	28	0.345	0.999	1	4.8
528	42	2	14	48	1.201	2.927	1	8.6
529	29	1	1	18	1.981	2.627	1	25.6
530	36	1	4	17	0.521	2.420	0	17.3
532	31	1	12	31	2.057	5.290	0	23.7
533	43	4	18	446	16.031	12.959	1	6.5
535	31	1	9	28	0.048	0.624	0	2.4
537	28	2	2	23	0.187	1.285	1	6.4
538	35	1	7	29	0.264	1.157	0	4.9
539	38	1	5	21	0.450	1.440	0	9.0
540	40	1	14	43	2.793	3.657	1	15.0
542	38	2	8	38	0.029	0.199	0	0.6
543	27	3	4	40	0.284	0.956	1	3.1
544	49	1	11	39	2.458	1.481	0	10.1
545	38	1	18	45	0.535	2.615	0	7.0
546	43	1	16	89	0.159	0.197	0	0.4
547	36	2	9	49	0.818	3.396	1	8.6
548	37	1	4	23	0.870	1.982	0	12.4
549	46	3	22	99	0.827	1.450	0	2.3
550	22	2	0	20	0.207	0.913	0	5.6
551	47	1	13	51	3.739	8.399	0	23.8
553	30	4	2	25	1.770	0.730	0	10.0
554	51	2	19	159	1.068	3.384	0	2.8
558	46	2	9	15	0.015	0.630	0	4.3
559	29	1	7	20	1.006	2.954	1	19.8
560	26	1	1	21	0.608	0.421	0	4.9
562	39	1	22	52	1.155	0.509	0	3.2
563	30	2	10	22	1.410	2.132	0	16.1
564	41	3	7	56	0.374	7.746	1	14.5
565	25	2	4	27	0.549	0.801	1	5.0
566	34	1	16	48	1.474	2.174	0	7.6
568	25	1	4	23	0.252	0.944	0	5.2
569	39	1	20	67	3.834	16.668	0	30.6
570	26	1	4	20	0.319	0.761	0	5.4
571	31	1	8	41	0.134	5.032	0	12.6
572	40	3	5	220	8.166	27.034	1	16.0
573	42	1	6	31	1.025	2.571	0	11.6
574	23	1	1	21	1.210	0.554	1	8.4
575	34	1	9	48	0.420	4.044	0	9.3
576	28	4	0	29	1.425	5.593	0	24.2
577	29	1	6	21	1.110	2.691	0	18.1
578	29	2	3	26	0.538	1.048	1	6.1
579	24	2	2	26	0.888	1.634	1	9.7
580	53	1	9	50	2.016	3.584	1	11.2
581	39	1	19	60	9.593	6.667	1	27.1
582	28	4	2	41	0.459	0.566	1	2.5
584	39	1	12	46	0.561	1.739	0	5.0
585	38	1	18	44	0.606	1.374	0	4.5
586	34	4	6	27	1.982	7.549	1	35.3
587	31	1	11	45	2.739	4.911	0	17.0
588	40	1	2	32	0.854	0.330	0	3.7
589	32	1	8	26	0.326	0.740	0	4.1
592	44	2	12	86	1.476	9.704	0	13.0
594	41	2	6	36	4.514	2.506	1	19.5
595	28	1	0	28	2.284	7.040	1	33.3
598	25	1	9	18	0.066	0.150	0	1.2
599	43	2	16	83	0.259	3.144	0	4.1
600	45	1	12	37	1.343	2.172	0	9.5
601	35	1	13	35	0.432	1.143	0	4.5
602	34	1	13	51	1.875	3.021	0	9.6
603	29	1	2	14	0.587	1.429	1	14.4
604	32	2	11	75	7.759	9.716	1	23.3
605	27	3	6	26	0.930	1.774	0	10.4
606	39	1	16	57	1.559	9.499	0	19.4
608	31	4	1	29	1.065	2.154	0	11.1
609	28	1	4	28	0.437	3.539	1	14.2
610	27	2	8	38	0.364	2.258	0	6.9
612	31	1	9	26	0.310	0.860	0	4.5
613	32	1	0	20	0.263	0.337	1	3.0
614	29	2	9	36	0.127	0.377	0	1.4
615	31	2	9	53	1.195	5.960	0	13.5
616	32	2	5	43	1.806	4.644	1	15.0
617	39	1	6	42	2.541	5.817	1	19.9
618	27	2	7	30	0.448	0.752	1	4.0
619	29	1	13	42	1.458	1.650	1	7.4
620	31	1	11	34	0.298	2.014	0	6.8
621	41	2	22	75	9.877	7.823	0	23.6
623	35	2	0	22	1.970	1.330	1	15.0
624	38	2	2	22	1.209	3.785	1	22.7
625	29	1	1	17	0.157	0.982	0	6.7
626	37	1	7	32	2.697	3.575	0	19.6
627	26	1	8	25	0.210	0.965	0	4.7
628	27	1	0	16	0.130	0.046	0	1.1
629	42	2	18	66	1.641	4.695	0	9.6
632	36	1	10	28	0.815	2.321	1	11.2
633	48	1	17	66	1.468	0.644	0	3.2
634	45	1	10	52	0.932	1.876	0	5.4
636	26	1	6	22	0.721	1.545	0	10.3
638	50	3	10	80	2.479	7.281	0	12.2
639	30	1	11	33	0.682	2.090	0	8.4
640	49	2	22	79	0.288	5.479	0	7.3
641	36	1	1	16	0.433	1.055	0	9.3
642	24	2	3	19	0.359	0.458	0	4.3
643	42	5	6	190	3.157	11.663	0	7.8
644	28	2	0	30	0.121	1.319	1	4.8
645	52	2	12	76	0.995	4.857	0	7.7
646	39	2	9	56	4.765	5.707	0	18.7
648	29	2	0	22	1.039	1.799	1	12.9
649	40	1	8	28	0.534	0.474	0	3.6
650	27	1	10	34	0.851	1.359	0	6.5
651	24	3	2	16	0.270	0.754	1	6.4
652	26	2	5	28	1.024	1.160	0	7.8
653	40	3	16	116	1.614	12.422	0	12.1
654	50	2	16	71	1.663	6.218	0	11.1
656	26	2	6	30	0.144	0.156	0	1.0
658	37	3	16	50	7.320	10.980	1	36.6
659	52	5	9	70	1.329	5.251	1	9.4
661	28	1	3	26	0.432	2.168	0	10.0
662	29	2	6	57	0.453	0.801	0	2.2
664	54	1	18	114	3.295	6.395	0	8.5
665	32	1	10	32	1.168	2.448	0	11.3
666	32	1	11	53	0.089	1.660	0	3.3
667	36	1	14	81	1.785	4.047	0	7.2
668	31	1	12	24	0.442	1.142	0	6.6
669	28	1	11	24	0.107	0.469	0	2.4
670	34	1	10	33	2.502	0.897	1	10.3
671	53	4	7	61	1.875	2.944	0	7.9
672	50	1	8	47	0.399	2.844	0	6.9
673	52	4	13	234	7.387	10.631	0	7.7
674	30	2	4	15	0.350	0.625	0	6.5
675	26	2	0	28	1.271	1.333	0	9.3
676	42	2	21	121	1.365	2.386	0	3.1
677	27	1	6	43	0.883	1.224	0	4.9
678	41	1	14	52	0.926	2.870	0	7.3
679	27	1	10	31	1.362	4.001	0	17.3
681	37	1	12	44	2.995	3.473	0	14.7
682	29	1	3	17	0.470	1.060	0	9.0
683	27	1	9	45	2.852	5.158	1	17.8
685	21	3	0	24	0.833	1.015	0	7.7
686	41	4	14	44	0.353	0.395	0	1.7
687	23	2	1	18	0.885	2.481	1	18.7
688	45	2	9	69	0.707	3.916	0	6.7
689	45	2	2	29	0.443	1.123	0	5.4
690	33	1	6	21	0.567	1.008	0	7.5
691	21	1	1	16	0.141	0.867	0	6.3
692	40	1	6	36	0.391	0.365	1	2.1
693	40	2	3	28	0.553	2.107	0	9.5
694	56	1	11	59	4.673	4.177	0	15.0
696	29	1	6	46	1.170	1.314	0	5.4
697	29	2	6	65	1.985	9.975	0	18.4
698	39	1	13	27	0.966	1.194	0	8.0
699	27	2	1	23	0.285	2.567	0	12.4
700	25	1	1	15	0.371	1.339	1	11.4
701	36	5	5	20	0.729	0.891	0	8.1
702	35	1	7	39	1.702	4.577	1	16.1
703	25	2	4	33	0.553	1.163	0	5.2
704	21	2	2	20	0.291	0.609	1	4.5
705	23	1	2	16	0.805	0.731	1	9.6
706	32	1	14	73	3.752	2.672	0	8.8
707	34	1	0	20	2.049	1.371	0	17.1
709	39	1	22	73	3.727	6.712	0	14.3
710	46	1	1	21	1.377	1.731	1	14.8
711	35	2	10	98	4.875	8.159	0	13.3
712	33	4	9	28	0.377	0.827	0	4.3
713	30	2	12	68	2.857	10.811	0	20.1
714	54	3	21	118	9.600	7.392	0	14.4
715	42	2	5	41	0.355	1.039	0	3.4
716	28	1	9	22	5.283	0.811	1	27.7
717	35	1	9	34	0.398	1.302	0	5.0
718	41	3	16	135	1.277	4.663	0	4.4
719	41	1	16	49	0.431	0.353	0	1.6
720	53	4	5	78	6.936	6.558	1	17.3
721	28	1	4	26	1.295	2.215	0	13.5
722	35	1	5	32	0.175	3.985	0	13.0
723	42	1	7	25	1.629	0.921	0	10.2
726	48	2	30	148	3.975	6.681	0	7.2
727	33	1	2	44	0.525	0.839	0	3.1
728	37	1	6	31	1.701	1.926	1	11.7
730	35	3	10	39	1.602	2.454	0	10.4
731	48	2	6	66	2.316	5.670	0	12.1
732	34	1	16	75	3.955	3.845	0	10.4
734	41	2	8	43	0.086	0.215	0	0.7
736	39	1	6	61	0.563	2.914	0	5.7
737	46	1	1	20	0.809	1.991	0	14.0
738	33	2	4	55	2.160	1.690	0	7.0
739	34	3	2	39	1.143	4.005	0	13.2
740	34	3	12	47	1.301	1.848	0	6.7
742	45	3	16	80	0.912	1.488	0	3.0
743	25	1	1	19	0.190	1.121	0	6.9
744	31	2	6	32	3.341	4.051	1	23.1
745	34	4	7	40	0.950	1.610	0	6.4
746	45	1	23	50	0.559	1.541	0	4.2
747	44	2	17	129	0.551	2.674	0	2.5
751	38	1	6	18	0.131	2.209	0	13.0
752	27	1	7	26	0.641	1.725	0	9.1
753	29	1	11	36	0.709	1.487	0	6.1
754	29	2	5	28	2.126	3.110	0	18.7
755	22	1	4	24	1.636	2.108	1	15.6
756	47	1	4	26	0.122	2.582	0	10.4
757	27	2	5	25	0.706	0.995	0	6.8
758	24	1	2	18	0.528	0.552	0	6.0
760	26	1	3	29	2.527	4.926	1	25.7
761	40	1	22	100	5.402	9.198	0	14.6
762	33	2	12	58	3.084	7.588	0	18.4
763	30	1	0	17	0.304	1.736	0	12.0
764	39	2	16	69	1.061	0.595	0	2.4
765	24	2	0	16	0.025	1.143	0	7.3
766	27	4	0	70	1.618	3.982	1	8.0
767	38	2	7	64	0.651	1.269	0	3.0
768	36	1	9	40	1.976	1.944	0	9.8
769	39	1	4	33	0.397	1.913	0	7.0
770	43	1	13	76	2.151	2.485	1	6.1
771	24	1	8	17	0.569	0.383	1	5.6
772	21	1	5	25	0.367	1.883	0	9.0
773	26	2	2	24	1.577	1.687	1	13.6
774	43	1	11	37	0.180	0.301	0	1.3
775	36	3	11	44	0.446	6.022	0	14.7
778	34	2	10	33	0.574	2.561	0	9.5
779	47	2	17	41	0.456	3.439	0	9.5
780	48	1	0	30	2.663	4.027	0	22.3
781	29	2	9	46	0.241	0.495	0	1.6
782	36	1	7	43	1.040	1.626	0	6.2
783	35	1	10	39	0.218	2.005	0	5.7
784	39	1	13	38	1.897	1.485	0	8.9
785	41	1	21	76	6.949	8.631	0	20.5
786	29	3	1	30	0.279	1.071	0	4.5
787	39	2	18	44	0.368	0.556	0	2.1
788	33	1	12	32	2.105	1.511	0	11.3
790	39	1	0	39	1.066	2.015	0	7.9
791	40	2	8	57	0.878	2.314	0	5.6
793	34	1	15	67	3.741	5.103	0	13.2
794	29	3	7	84	6.912	4.512	1	13.6
795	30	1	0	17	0.447	0.182	1	3.7
796	38	3	3	25	0.312	0.613	0	3.7
797	25	4	0	24	1.597	1.307	1	12.1
798	26	2	6	35	0.086	0.579	0	1.9
800	29	1	8	24	0.470	1.138	0	6.7
801	28	1	5	22	0.140	0.476	0	2.8
802	48	1	30	101	1.875	4.589	0	6.4
803	26	1	0	14	0.302	0.748	1	7.5
804	36	1	4	23	1.327	2.422	0	16.3
805	30	1	12	38	0.171	3.059	0	8.5
806	41	1	1	19	0.242	0.594	0	4.4
807	34	3	13	52	1.497	3.287	0	9.2
809	45	1	17	62	2.437	6.863	0	15.0
810	22	1	4	19	1.887	2.502	1	23.1
813	34	1	6	20	0.042	0.198	0	1.2
814	31	1	10	49	3.237	1.565	1	9.8
815	33	1	7	22	0.631	0.777	0	6.4
816	40	2	2	36	0.278	1.090	0	3.8
817	36	2	6	27	0.262	0.980	1	4.6
820	36	1	7	40	1.695	2.265	0	9.9
822	32	1	16	38	0.694	7.286	0	21.0
823	45	1	3	20	0.105	0.315	0	2.1
824	27	4	0	25	1.419	1.756	1	12.7
825	41	2	4	26	1.473	3.519	1	19.2
827	48	1	13	50	6.114	9.286	1	30.8
828	50	1	1	26	1.852	1.866	0	14.3
829	45	3	0	22	0.030	0.894	0	4.2
830	33	2	2	37	0.834	0.831	0	4.5
831	33	1	13	52	2.714	8.362	1	21.3
832	27	2	8	18	0.401	1.741	0	11.9
833	36	2	7	43	2.649	3.973	0	15.4
834	30	4	7	30	0.264	4.446	0	15.7
835	28	2	3	36	0.384	2.712	0	8.6
837	23	2	3	24	0.552	0.960	0	6.3
838	23	1	7	22	0.849	2.319	0	14.4
839	26	1	10	25	1.306	0.469	0	7.1
840	31	1	8	22	0.370	1.104	0	6.7
841	38	3	13	25	0.343	1.082	0	5.7
842	29	3	7	63	0.572	2.893	0	5.5
843	32	1	14	36	0.273	0.591	0	2.4
844	32	2	8	45	0.982	0.683	0	3.7
845	41	1	7	43	0.694	1.198	0	4.4
847	28	2	7	34	0.359	2.021	0	7.0
848	25	4	0	18	2.802	3.210	1	33.4
849	32	1	12	28	0.116	0.696	0	2.9
850	52	1	16	64	1.866	3.638	0	8.6

Implementasi Clustering

K-Modes

set.seed(123)

# discretization menggunakan ntile
df_kmodes_data <- df %>% mutate(across(everything(), ~as.factor(ntile(., 3)))) %>% as.data.frame()

# pastikan semua factor
df_kmodes_data <- data.frame(lapply(df_kmodes_data, as.factor))

# model K-Modes
kmodes_model <- klaR::kmodes(df_kmodes_data, modes = 3)

# simpan hasil
df_final <- df %>% mutate(Cluster_KModes = as.factor(kmodes_model$cluster))

# tabel hasil
df_final %>% 
  group_by(Cluster_KModes) %>% 
  summarise(Jumlah_Anggota = n()) %>% 
  kable(caption = "Tabel 4: Distribusi Anggota Cluster - K-Modes") %>% 
  kable_styling(bootstrap_options = c("striped", "condensed"), full_width = F)

Tabel 4: Distribusi Anggota Cluster - K-Modes

Cluster_KModes	Jumlah_Anggota
1	214
2	236
3	250

Fuzzy C-Means

set.seed(123)

# data digunakan dalam bentuk matriks dan sudah di scaling
fcm_model <- e1071::cmeans(as.matrix(df_scaled), centers = 3, m = 2)

# menyimpan hasil cluster ke dalam dataset
df_final$Cluster_FCM <- as.factor(fcm_model$cluster)

#tabel hasil
df_final %>% 
  group_by(Cluster_FCM) %>% 
  summarise(Jumlah_Anggota = n()) %>% 
  kable(caption = "Tabel 5: Distribusi Cluster Fuzzy C-Means") %>% 
  kable_styling(bootstrap_options = c("striped", "condensed"), full_width = F)

Tabel 5: Distribusi Cluster Fuzzy C-Means

Cluster_FCM	Jumlah_Anggota
1	184
2	187
3	329

Mean Shift

set.seed(123)

# semakin kecil bandwidth, cluster lebih banyak (lebih detail)
ms_model <- meanShiftR::meanShift(queryData = as.matrix(df_scaled), trainData = as.matrix(df_scaled), bandwidth = rep(0.9, ncol(df_scaled)))

# menyimpan hasil cluster ke dataset# Menyimpan hasil cluster ke dataset
df_final$Cluster_MeanShift <- as.factor(ms_model$assignment)

# tabel hasil
df_final %>% 
  group_by(Cluster_MeanShift) %>% 
  summarise(Jumlah_Anggota = n()) %>% 
  kable(caption = "Tabel 6: Distribusi Cluster Mean Shift") %>% 
  kable_styling(bootstrap_options = c("striped", "condensed"), full_width = F)

Tabel 6: Distribusi Cluster Mean Shift

Cluster_MeanShift	Jumlah_Anggota
1	254
2	1
3	1
4	1
5	4
6	3
7	1
8	27
9	1
10	81
11	7
12	5
13	7
14	2
15	1
16	2
17	1
18	11
19	7
20	1
21	1
22	1
23	2
24	1
25	3
26	2
27	118
28	1
29	4
30	1
31	1
32	3
33	1
34	1
35	11
36	7
37	1
38	1
39	5
40	1
41	1
42	1
43	1
44	1
45	1
46	1
47	3
48	1
49	3
50	6
51	1
52	1
53	5
54	6
55	3
56	1
57	1
58	1
59	2
60	2
61	1
62	1
63	2
64	1
65	2
66	1
67	1
68	1
69	1
70	2
71	1
72	2
73	1
74	3
75	2
76	1
77	1
78	1
79	1
80	2
81	1
82	1
83	1
84	1
85	1
86	1
87	1
88	1
89	1
90	2
91	2
92	1
93	1
94	1
95	1
96	1
97	1
98	1
99	1
100	1
101	1
102	1
103	1
104	1
105	1
106	1
107	1
108	1
109	1
110	1
111	1
112	1
113	1
114	1
115	1
116	1
117	1
118	1
119	1
120	1
121	1
122	1
123	1
124	1

DBSCAN

set.seed(123)

# KNN Distance Plot
kNNdistplot(df_scaled, k = 5)
abline(h = 1.1, col = "red", lty = 2)
title("KNN Distance Plot untuk Menentukan eps")

db_model <- dbscan::dbscan(df_scaled, eps = 1.1, minPts = 5)

# menyimpan hasil cluster ke dataset
df_final$Cluster_DBSCAN <- as.factor(db_model$cluster)

# tabel hasil
df_final %>% 
  group_by(Cluster_DBSCAN) %>% 
  summarise(Jumlah_Anggota = n()) %>% 
  mutate(Keterangan = ifelse(Cluster_DBSCAN == 0, "Noise (Outlier)", "Cluster Utama")) %>% 
  kable(caption = "Tabel 7: Distribusi Cluster DBSCAN") %>% 
  kable_styling(bootstrap_options = c("striped", "condensed"), full_width = F)

Tabel 7: Distribusi Cluster DBSCAN

Cluster_DBSCAN	Jumlah_Anggota	Keterangan
0	194	Noise (Outlier)
1	381	Cluster Utama
2	13	Cluster Utama
3	95	Cluster Utama
4	11	Cluster Utama
5	6	Cluster Utama

Evaluasi

summary_comp <- data.frame(Metode = c("K-Modes", "Fuzzy C-Means", "Mean Shift", "DBSCAN"), Jumlah_Cluster = c(
    length(unique(kmodes_model$cluster)),
    length(unique(fcm_model$cluster)),
    length(unique(ms_model$assignment)),
    length(unique(db_model$cluster[db_model$cluster != 0]))))

# tabel hasil
summary_comp %>% kable(caption = "Tabel 8: Perbandingan Jumlah Cluster yang Terbentuk") %>% kable_styling(bootstrap_options = c("bordered", "hover"), full_width = F)

Tabel 8: Perbandingan Jumlah Cluster yang Terbentuk

Metode	Jumlah_Cluster
K-Modes	3
Fuzzy C-Means	3
Mean Shift	124
DBSCAN	5