GitHub - AVJdataminer/DTW: Dynamic Time Warping model with hierarchical clustering

Dynamic Time Warping with hiererchical clustering

Create directories with date and versions

## Create directories first
t=format(Sys.time(), "%d_%b_%Y")
dir.create(paste("C:/MinuteClinic/Dynamic_time_warping/modeling_work_",t,sep=""))
setwd(paste("C:/MinuteClinic/Dynamic_time_warping/modeling_work_",t,sep=""))
subDir=paste(getwd())
version_path=paste(subDir,"/version_name",sep="")
ifelse(file.exists(version_path),,dir.create(file.path(version_path)))
indata_path=paste(subDir,"/dtw_indata",sep="")
ifelse(file.exists(indata_path),,dir.create(file.path(indata_path)))

modelpath=version_path
outpath=paste(subDir,"/version_name/output",sep="")
ifelse(file.exists(outpath),,dir.create(file.path(outpath)))
#create figures folder
#figures=paste(subDir,"/version_name/figures",sep="")
#ifelse(file.exists(figures),,dir.create(file.path(figures)))

list.files()
setwd(modelpath)
list.files()

TRUE

'dtw_indata'
'version_name'

'output'

Create a time series data set from hourly data

Starting with visit level data. Create an hourly aggregated data set for analysis.

require(dplyr)
require(lubridate)
# Develop best version hourly data ------
options(warn=0)
visit_level=read.csv('C:/MinuteClinic/Data/MC_data_01JAN2016_to_02JUL2018.csv')
visit_level$Employee=ifelse(visit_level$VisitOrigin=='Employee',1,0)
visit_level$Hmpil=ifelse(visit_level$VisitOrigin=='Hmpil',1,0)
visit_level$Scheduled=ifelse(visit_level$VisitOrigin=='Scheduled',ifelse(visit_level$ScheduleVisitType=="PSS",1,0),0)
visit_level$WalkIn=ifelse(visit_level$VisitOrigin=='Walk-In',1,0)
visit_level$RevenueGenerating=ifelse(visit_level$VisitStatus=='Revenue Generating',1,0)
visit_level$CompleteNonGenerating=ifelse(visit_level$VisitStatus=='Complete Non Revenue',1,0)
visit_level$ReferOutNoCharge=ifelse(visit_level$VisitStatus=='Refer Out No Charge',1,0)
visit_level$Canceled=ifelse(visit_level$VisitStatus=='Canceled',1,0)
visit_level$year=substr(visit_level$AppointmentDate,1,4)
visit_level$day=substr(visit_level$AppointmentDate,5,6)
visit_level$week=week(ymd(visit_level$AppointmentDate))
visit_level$Wait.Time=ifelse(visit_level$Canceled==1,0.01,visit_level$Wait.Time)
#subset to after Q4 of 2017
visit_level=filter(visit_level, AppointmentDate>=20171001)
visit_level$Hour=visit_level$AppointmentTimeOfDay/10000
visit_level=filter(visit_level, Hour>8 & Hour<=20)
visit_level$Hour=round(visit_level$Hour)
head(visit_level)

Loading required package: dplyr

Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

Loading required package: lubridate

Attaching package: 'lubridate'

The following object is masked from 'package:base':

    date

AppointmentDate	ClinicNumber	ProviderEpicid	VisitStatus	AppointmentTimeOfDay	AppointmentMadeTimeOfDay	Wait.Time	VisitOrigin	ScheduleTimeOfDay	...	WalkIn	RevenueGenerating	year	day	week	Hour
20171001	02497	3323	Revenue Generating	104000	103714	0.033333	Walk-In	1040	...	1	1	2017	10	40	10
20171001	02497	3323	Revenue Generating	121000	120056	0.066666	Walk-In	1210	...	1	1	2017	10	40	12
20171003	07017	1079	Revenue Generating	122500	121610	20.150000	Walk-In	NA	...	1	1	2017	10	40	12
20171005	22063	1398	Revenue Generating	113000	112358	0.066666	Walk-In	1130	...	1	1	2017	10	40	11
20171005	22063	1398	Revenue Generating	131000	130313	0.066666	Walk-In	1310	...	1	1	2017	10	40	13
20171005	22063	1398	Revenue Generating	142000	141007	0.050000	Walk-In	1420	...	1	1	2017	10	40	14

#convert visit level to daily to calc percent visits per hour
hourly=data.frame(visit_level%>%group_by(ClinicNumber,AppointmentDate,Hour)%>%summarise(provider.count=n_distinct(ProviderEpicid),
        Employee_Sum=sum(Employee),Hmpil_Sum=sum(Hmpil),Scheduled_Sum=sum(Scheduled),WalkIn_sum=sum(WalkIn),
        ReferOutNoCharge_Sum=sum(ReferOutNoCharge),Canceled_Sum=sum(Canceled),wait_time_avg=mean(Wait.Time,na.rm=T)))
head(hourly)

ClinicNumber	AppointmentDate	Hour	provider.count	Hmpil_Sum	WalkIn_sum	Canceled_Sum	wait_time_avg
00012	20171001	10	1	0	7	0	7.128571
00012	20171001	11	2	1	2	1	5.231111
00012	20171001	12	1	0	2	0	1.424999
00012	20171001	13	1	0	2	0	23.191666
00012	20171001	14	1	0	5	0	40.020000
00012	20171001	15	1	0	2	0	3.666666

hourly$AllVisits=hourly$Employee_Sum+hourly$Hmpil_Sum+hourly$Scheduled_Sum+hourly$WalkIn_sum
head(hourly)
daily=hourly%>%group_by(ClinicNumber, AppointmentDate)%>%summarise(daily_sum=sum(AllVisits))
head(daily)

ClinicNumber	AppointmentDate	Hour	provider.count	Hmpil_Sum	WalkIn_sum	Canceled_Sum	wait_time_avg	AllVisits
00012	20171001	10	1	0	7	0	7.128571	7
00012	20171001	11	2	1	2	1	5.231111	3
00012	20171001	12	1	0	2	0	1.424999	2
00012	20171001	13	1	0	2	0	23.191666	2
00012	20171001	14	1	0	5	0	40.020000	5
00012	20171001	15	1	0	2	0	3.666666	2

ClinicNumber	AppointmentDate	daily_sum
00012	20171001	25
00012	20171002	40
00012	20171003	23
00012	20171004	35
00012	20171005	34
00012	20171006	38

hrly=left_join(select(hourly,ClinicNumber, AppointmentDate,Hour,AllVisits), daily, by=c("ClinicNumber","AppointmentDate"))
hrly=unique(hrly)
hrly$PerVisits=hrly$AllVisits/hrly$daily_sum
head(hrly)

ClinicNumber	AppointmentDate	Hour	AllVisits	daily_sum	PerVisits
00012	20171001	10	7	25	0.28
00012	20171001	11	3	25	0.12
00012	20171001	12	2	25	0.08
00012	20171001	13	2	25	0.08
00012	20171001	14	5	25	0.20
00012	20171001	15	2	25	0.08

#check the data
range(hrly$AppointmentDate)
hrly=filter(hrly, AppointmentDate<20180101)
hrly_out=data.frame(hrly%>%group_by(ClinicNumber,Hour)%>%summarise(per_visits=mean(PerVisits, na.rm=T)))
summary(hrly_out)
write.csv(hrly_out, 'C:/MinuteClinic/Dynamic_time_warping/DTW_Input_data/hourly_percent_visits_2018.csv', row.names=F)

20171001
20171231

  ClinicNumber        Hour         per_visits     
 00012  :   13   Min.   : 8.00   Min.   :0.00000  
 00015  :   13   1st Qu.:11.00   1st Qu.:0.08849  
 00017  :   13   Median :14.00   Median :0.11101  
 00019  :   13   Mean   :13.74   Mean   :0.10735  
 00033  :   13   3rd Qu.:17.00   3rd Qu.:0.12753  
 00069  :   13   Max.   :20.00   Max.   :1.00000  
 (Other):13567                   NA's   :7

memory.limit()
rm(list=ls())
gc()

7603

	used	(Mb)	gc trigger	(Mb)	max used	(Mb)
Ncells	634472	33.9	3763080	201.0	4703850	251.3
Vcells	3063194	23.4	283334255	2161.7	354149013	2702.0

require(lubridate)
hrly=read.csv('C:/MinuteClinic/Dynamic_time_warping/DTW_Input_data/hourly_percent_visits_2018.csv')
head(hrly)
hrly=filter(hrly, Hour>=9 & Hour <19)
#install.packages('Hmisc')
Hmisc::describe(hrly)

ClinicNumber	Hour	per_visits
00012	8	0.04038461
00012	9	0.07950237
00012	10	0.10117204
00012	11	0.11112025
00012	12	0.12879019
00012	13	0.06822577

hrly 

 3  Variables      11221  Observations
--------------------------------------------------------------------------------
ClinicNumber 
       n  missing distinct 
   11221        0     1133 

lowest : 00012 00015 00017 00019 00022, highest: 22063 22067 MC26  MC960 MC98 
--------------------------------------------------------------------------------
Hour 
       n  missing distinct     Info     Mean      Gmd      .05      .10 
   11221        0       10     0.99    13.49    3.298        9        9 
     .25      .50      .75      .90      .95 
      11       13       16       17       18 
                                                                      
Value          9    10    11    12    13    14    15    16    17    18
Frequency   1127  1125  1125  1123  1122  1123  1125  1122  1119  1110
Proportion 0.100 0.100 0.100 0.100 0.100 0.100 0.100 0.100 0.100 0.099
--------------------------------------------------------------------------------
per_visits 
       n  missing distinct     Info     Mean      Gmd      .05      .10 
   11214        7    11207        1   0.1179  0.02845  0.07580  0.08813 
     .25      .50      .75      .90      .95 
 0.10193  0.11667  0.13057  0.14605  0.15838 

lowest : 0.03225806 0.03311966 0.03333333 0.03367003 0.03846154
highest: 0.43598821 0.50000000 0.66666667 0.75000000 1.00000000
--------------------------------------------------------------------------------

#check for missing hour
counts=data.frame(table(hrly$ClinicNumber))
#counts

#create base size dataframe
clinic_list=as.character(unique(hrly$ClinicNumber))
df1=data.frame(rep(clinic_list,each=10),rep(9:18,length(clinic_list)))
head(df1)
names(df1)<-c("ClinicNumber","Hour")
df1$ClinicNumber=as.character(df1$ClinicNumber)
hrly$ClinicNumber=as.character(hrly$ClinicNumber)
df2=left_join(df1,hrly, by=c("ClinicNumber", "Hour"))
head(df2)

rep.clinic_list..each...10.	rep.9.18..length.clinic_list..
00012	9
00012	10
00012	11
00012	12
00012	13
00012	14

ClinicNumber	Hour	per_visits
00012	9	0.07950237
00012	10	0.10117204
00012	11	0.11112025
00012	12	0.12879019
00012	13	0.06822577
00012	14	0.08772773

df2[is.na(df2)] <- 0
Hmisc::describe(df2)

df2 

 3  Variables      11330  Observations
--------------------------------------------------------------------------------
ClinicNumber 
       n  missing distinct 
   11330        0     1133 

lowest : 00012 00015 00017 00019 00022, highest: 22063 22067 MC26  MC960 MC98 
--------------------------------------------------------------------------------
Hour 
       n  missing distinct     Info     Mean      Gmd      .05      .10 
   11330        0       10     0.99     13.5      3.3      9.0      9.9 
     .25      .50      .75      .90      .95 
    11.0     13.5     16.0     17.1     18.0 
                                                            
Value         9   10   11   12   13   14   15   16   17   18
Frequency  1133 1133 1133 1133 1133 1133 1133 1133 1133 1133
Proportion  0.1  0.1  0.1  0.1  0.1  0.1  0.1  0.1  0.1  0.1
--------------------------------------------------------------------------------
per_visits 
       n  missing distinct     Info     Mean      Gmd      .05      .10 
   11330        0    11208        1   0.1167  0.03026  0.07315  0.08674 
     .25      .50      .75      .90      .95 
 0.10135  0.11640  0.13044  0.14590  0.15818 

lowest : 0.00000000 0.03225806 0.03311966 0.03333333 0.03367003
highest: 0.43598821 0.50000000 0.66666667 0.75000000 1.00000000
--------------------------------------------------------------------------------

#full clean dataset
write.csv(df2,'C:/MinuteClinic/Dynamic_time_warping/DTW_Input_data/hourly_percent_visits_Q42017_clean.csv', row.names=F)
head(df2)

ClinicNumber	Hour	per_visits
00012	9	0.07950237
00012	10	0.10117204
00012	11	0.11112025
00012	12	0.12879019
00012	13	0.06822577
00012	14	0.08772773

indata=data.frame(matrix(df2$per_visits,nrow = (nrow(df2)/1133),ncol = 1133, byrow = F))
head(indata)
dim(indata)
names(indata)<-clinic_list
head(indata)
write.csv(indata,'C:/MinuteClinic/Dynamic_time_warping/DTW_Input_data/hourly_percent_visits_Q42017_column_wise.csv', row.names=F)

X1	X2	X3	X4	X5	X6	X7	X8	X9	X10	...	X1124	X1125	X1126	X1127	X1128	X1129	X1130	X1131	X1132	X1133
0.07950237	0.08660912	0.09182722	0.09255609	0.2100011	0.08324442	0.09708343	0.10065626	0.08714902	0.10045744	...	0.09186785	0.1050256	0.08468987	0.4166667	0.2000000	0.1153846	0.062500	0.1910700	0.1944730	0.2063808
0.10117204	0.11938805	0.12762900	0.12588859	0.2179389	0.11400472	0.12718215	0.12386541	0.12393098	0.12556233	...	0.10410890	0.1351720	0.10708249	0.3928571	0.0000000	0.2307692	0.156250	0.2075086	0.1831905	0.2195078
0.11112025	0.10078731	0.10567166	0.10958468	0.1934753	0.10677515	0.10940930	0.12006374	0.11926979	0.10829557	...	0.10850045	0.1458302	0.14667971	0.0000000	0.0000000	0.2692308	0.125000	0.1708053	0.1517759	0.1650625
0.12879019	0.11806513	0.12734586	0.12343345	0.1695541	0.13091189	0.12049299	0.12076218	0.11667445	0.10636356	...	0.15694842	0.1386013	0.18225043	0.0000000	0.6666667	0.0000000	0.171875	0.2303108	0.1668078	0.2484773
0.06822577	0.05750729	0.07647690	0.06655861	0.2203184	0.06758471	0.07530850	0.08395549	0.07342130	0.08027248	...	0.12346782	0.1014109	0.09603915	0.0000000	0.0000000	0.1538462	0.062500	0.2072596	0.1178391	0.1468982
0.08772773	0.09535318	0.10385227	0.10551818	0.1745796	0.09538082	0.13993317	0.15060871	0.11085450	0.12323391	...	0.15328175	0.1472172	0.13435042	0.0000000	0.0000000	0.2307692	0.171875	0.2210729	0.1590159	0.1734248

10
1133

00012	00015	00017	00019	00022	00033	00038	00041	00069	00073	...	17661	17663	17665	22052	22053	22063	22067	MC26	MC960	MC98
0.07950237	0.08660912	0.09182722	0.09255609	0.2100011	0.08324442	0.09708343	0.10065626	0.08714902	0.10045744	...	0.09186785	0.1050256	0.08468987	0.4166667	0.2000000	0.1153846	0.062500	0.1910700	0.1944730	0.2063808
0.10117204	0.11938805	0.12762900	0.12588859	0.2179389	0.11400472	0.12718215	0.12386541	0.12393098	0.12556233	...	0.10410890	0.1351720	0.10708249	0.3928571	0.0000000	0.2307692	0.156250	0.2075086	0.1831905	0.2195078
0.11112025	0.10078731	0.10567166	0.10958468	0.1934753	0.10677515	0.10940930	0.12006374	0.11926979	0.10829557	...	0.10850045	0.1458302	0.14667971	0.0000000	0.0000000	0.2692308	0.125000	0.1708053	0.1517759	0.1650625
0.12879019	0.11806513	0.12734586	0.12343345	0.1695541	0.13091189	0.12049299	0.12076218	0.11667445	0.10636356	...	0.15694842	0.1386013	0.18225043	0.0000000	0.6666667	0.0000000	0.171875	0.2303108	0.1668078	0.2484773
0.06822577	0.05750729	0.07647690	0.06655861	0.2203184	0.06758471	0.07530850	0.08395549	0.07342130	0.08027248	...	0.12346782	0.1014109	0.09603915	0.0000000	0.0000000	0.1538462	0.062500	0.2072596	0.1178391	0.1468982
0.08772773	0.09535318	0.10385227	0.10551818	0.1745796	0.09538082	0.13993317	0.15060871	0.11085450	0.12323391	...	0.15328175	0.1472172	0.13435042	0.0000000	0.0000000	0.2307692	0.171875	0.2210729	0.1590159	0.1734248

#let's create the dataset as rowwise with hour across the top
rdata=data.frame(matrix(df2$per_visits,nrow =1133,ncol = 10, byrow = F))
head(rdata)
dim(rdata)

X1	X2	X3	X4	X5	X6	X7	X8	X9	X10
0.07950237	0.13953903	0.1294059	0.1049971	0.10328918	0.1154648	0.11143828	0.12894046	0.08395457	0.12655779
0.10117204	0.06502419	0.1250171	0.1055075	0.11953988	0.1106251	0.11387425	0.12945934	0.11490577	0.10256979
0.11112025	0.10560313	0.1034505	0.1369112	0.06265098	0.1230172	0.09043376	0.12664228	0.11448791	0.10633368
0.12879019	0.11710577	0.1137967	0.1256704	0.09814131	0.0944434	0.11907518	0.07421062	0.12783914	0.09441497
0.06822577	0.11897637	0.1270601	0.1263911	0.10574567	0.1090297	0.11621321	0.09758311	0.10665360	0.12550554
0.08772773	0.10618040	0.1511323	0.1076241	0.12465133	0.1031845	0.11686510	0.11184903	0.12159527	0.12267545

1133
10

#install.packages('dtw')
require(dtw)
dmatrix=(dist(rdata, method="DTW"))
clusters<-hclust(dmatrix,method="average")
str(clusters)

List of 7
 $ merge      : int [1:1132, 1:2] -286 -116 -138 -373 -93 -141 -703 -951 -186 -421 ...
 $ height     : num [1:1132] 0.0557 0.057 0.0572 0.0608 0.0614 ...
 $ order      : int [1:1133] 1077 270 271 1073 1080 1064 1065 971 520 235 ...
 $ labels     : NULL
 $ method     : chr "average"
 $ call       : language hclust(d = dmatrix, method = "average")
 $ dist.method: chr "DTW"
 - attr(*, "class")= chr "hclust"

dim(dmatrix)
plot(clusters)

1133
1133

# Cut tree into 10 groups
sub_grp <- cutree(clusters, k = 10)
sub_grp2<- cutree(clusters, k = 2)
sub_grp3 <- cutree(clusters, k = 3)
sub_grp4 <- cutree(clusters, k = 4)
sub_grp5 <- cutree(clusters, k = 5)
sub_grp6<- cutree(clusters, k = 6)
sub_grp7 <- cutree(clusters, k = 7)
sub_grp8 <- cutree(clusters, k = 8)
sub_grp9 <- cutree(clusters, k = 9)
# Number of members in each cluster
table(sub_grp)
table(sub_grp3)
table(sub_grp5)

sub_grp
   1    2    3    4    5    6    7    8    9   10 
1118    3    1    2    1    1    1    1    4    1 



sub_grp3
   1    2    3 
1131    1    1 



sub_grp5
   1    2    3    4    5 
1124    6    1    1    1

clustdf=rdata %>%
  mutate(cluster10 = sub_grp, cluster2=sub_grp2,cluster3 = sub_grp3, cluster4=sub_grp4,cluster5 = sub_grp5, cluster6=sub_grp6,
        cluster7= sub_grp7, cluster8=sub_grp8,cluster9 = sub_grp9)
clustdf$ClinicNumber<-clinic_list
head(clustdf)
write.csv(clustdf,'C:/MinuteClinic/Dynamic_time_warping/hourly_percent_visits_Q42017_clusters.csv')
#table(clustdf$cluster,clustdf$ClinicNumber)

X1	X2	X3	X4	X5	X6	X7	X8	X9	X10	cluster10	cluster2	cluster3	cluster4	cluster5	cluster6	cluster7	cluster8	cluster9	ClinicNumber
0.07950237	0.13953903	0.1294059	0.1049971	0.10328918	0.1154648	0.11143828	0.12894046	0.08395457	0.12655779	1	1	1	1	1	1	1	1	1	00012
0.10117204	0.06502419	0.1250171	0.1055075	0.11953988	0.1106251	0.11387425	0.12945934	0.11490577	0.10256979	1	1	1	1	1	1	1	1	1	00015
0.11112025	0.10560313	0.1034505	0.1369112	0.06265098	0.1230172	0.09043376	0.12664228	0.11448791	0.10633368	1	1	1	1	1	1	1	1	1	00017
0.12879019	0.11710577	0.1137967	0.1256704	0.09814131	0.0944434	0.11907518	0.07421062	0.12783914	0.09441497	1	1	1	1	1	1	1	1	1	00019
0.06822577	0.11897637	0.1270601	0.1263911	0.10574567	0.1090297	0.11621321	0.09758311	0.10665360	0.12550554	1	1	1	1	1	1	1	1	1	00022
0.08772773	0.10618040	0.1511323	0.1076241	0.12465133	0.1031845	0.11686510	0.11184903	0.12159527	0.12267545	1	1	1	1	1	1	1	1	1	00033

#create long data
dflong=left_join(df2,select(clustdf,cluster10,cluster2,cluster3,cluster4,cluster5,cluster6,cluster7,cluster8,
                            cluster9,ClinicNumber), by="ClinicNumber")
head(dflong)
#plot the cluster groups with a few number of clusters by clinic by hour
write.csv(dflong,'C:/MinuteClinic/Dynamic_time_warping/hourly_percent_visits_Q42017_clusters_longformat.csv')

ClinicNumber	Hour	per_visits	cluster10	cluster2	cluster3	cluster4	cluster5	cluster6	cluster7	cluster8	cluster9
00012	9	0.07950237	1	1	1	1	1	1	1	1	1
00012	10	0.10117204	1	1	1	1	1	1	1	1	1
00012	11	0.11112025	1	1	1	1	1	1	1	1	1
00012	12	0.12879019	1	1	1	1	1	1	1	1	1
00012	13	0.06822577	1	1	1	1	1	1	1	1	1
00012	14	0.08772773	1	1	1	1	1	1	1	1	1

ggplot(dflong, aes(x=Hour, y=per_visits)) + 
    geom_line(aes(colour=as.factor(cluster10), group=as.factor(cluster10))) + # colour, group both depend on cond2
    geom_point(aes(colour=as.factor(cluster10)),               # colour depends on cond2
               size=3)+facet_wrap(~cluster10)+theme_bw()

#install.packages("factoextra")
#library(factoextra)
fviz_nbclust(rdata, FUN = hcut, method = "wss")

Warning message in stats::dist(x):
"NAs introduced by coercion"Warning message in stats::dist(x, method = method, ...):
"NAs introduced by coercion"Warning message in stats::dist(x, method = method, ...):
"NAs introduced by coercion"Warning message in stats::dist(x, method = method, ...):
"NAs introduced by coercion"Warning message in stats::dist(x, method = method, ...):
"NAs introduced by coercion"Warning message in stats::dist(x, method = method, ...):
"NAs introduced by coercion"Warning message in stats::dist(x, method = method, ...):
"NAs introduced by coercion"Warning message in stats::dist(x, method = method, ...):
"NAs introduced by coercion"Warning message in stats::dist(x, method = method, ...):
"NAs introduced by coercion"Warning message in stats::dist(x, method = method, ...):
"NAs introduced by coercion"Warning message in stats::dist(x, method = method, ...):
"NAs introduced by coercion"

#install.packages('gplots')
#library(gplots) 
y <- matrix(rnorm(500), 100, 5, dimnames=list(paste("g", 1:100, sep=""), paste("t", 1:5, sep=""))) 
#head(y)
#heatmap.2(y)
heatmap.2(as.matrix(rdata))

plot(as.dendrogram(clusters), edgePar=list(col=3, lwd=4), horiz=T)

install.packages('')
library(pheatmap); library("RColorBrewer")
pheatmap(clusterdf, color=brewer.pal(9,"Blues"))

Error in library(pheatmap): there is no package called 'pheatmap'
Traceback:


1. library(pheatmap)

2. stop(txt, domain = NA)

Name		Name	Last commit message	Last commit date
Latest commit History 4 Commits
DTW MC R 30JUL2018.ipynb		DTW MC R 30JUL2018.ipynb
DTW MC R 30JUL2018.md		DTW MC R 30JUL2018.md
DTW+MC+R+30JUL2018.html		DTW+MC+R+30JUL2018.html
DTW+MC+R+30JUL2018.r		DTW+MC+R+30JUL2018.r
README.md		README.md
output_18_1.png		output_18_1.png
output_22_1.png		output_22_1.png
output_23_2.png		output_23_2.png
output_24_0.png		output_24_0.png
output_25_0.png		output_25_0.png

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

Repository files navigation

Dynamic Time Warping with hiererchical clustering

Create directories with date and versions

Create a time series data set from hourly data

About

Releases

Packages

Languages

AVJdataminer/DTW

Folders and files

Latest commit

History

Repository files navigation

Dynamic Time Warping with hiererchical clustering

Create directories with date and versions

Create a time series data set from hourly data

About

Topics

Resources

Stars

Watchers

Forks

Releases

Packages 0

Languages

Packages