This video demonstrates how to conduct a K-Means Cluster Analysis in SPSS. A K-Means Cluster Analysis allows the division of items into clusters based on specified variables.
Hi, can you interpret what these clusters mean for the example ID's? so for example the second cluster of patients have very high dep and sub. use , but score low in anxiety? is this correct? thanks Dr. Todd
This example was outstanding. I have DV three subscales that I have standardized: Self Esteem, Leadership Efficacy, and Leadership Activism. I have one Categorical IV: Type of mentoring Program: A) One-on-One, B) Team Mentoring, C) Online mentoring, D)Group Mentoring. I'm interested in knowing where these scores (low-high) fall under each Mentoring Type. Ran the analysis. How do I link the cluster to the mentoring type? In the first analysis I used a default cluster, Second analysis, I created 4 groups, which were significant. Should I then create a crosstab of Mentor Type by Group?
Hi, I was wondering how to read in cluster centers from an external file (after having done the hierarchical clustering) as SPSS always shows error messages (not correct format or one variable name is incorrect). Do you have a video for that? or any solution to my problem?
Thanks for the video! please, how do I combine k-means with mlp in an ensemble form using ibm spss? Also how do I find the ideal cluster number for k means?
can u explain the need to standardise the variables in this case? it seems they are more or less within the same range, so why the need to standardise?
Thanks for the video. I have two questions: 1- Why did you perform a standardize method at a very early stage? 2- Is the standardize method recommended always or only for some cases?
1. for me this is the case of being able to compare the results; can you imagine interpreting a graph where one variable is between 1 -100, and the second between 20000 - 100000? That'd be very difficult to visualise and interpret. but I'm sure there's a mathematical reason too. 2. I'd go for it every time, but you could probably skip it if your variables are all in the same range (1-100).
By standardizing the score you shift from comparing the values on the original measures to a comparison of cases relative positions on the measures relative to the mean for all cases. You are looking for clusters of cases based on how they relate to the average on the measures. So a cluster with cases that are high on two measures and low on another becomes a cluster that are, for example, at or above average on two measures and below average on the other.
@@MonikaS1890 as op i got a little bit confused as to why standardisation was necessary in this case because it appears all variables Dr Grande is using are within more or less the same range. but i assume he was maybe showing us a more rule of thumb approach or smth like that
I wish I had never studied data science. This is painfully boring. But I Love you Todd youre saving my life frfr
Yeah indeed sometimes i´m also asking myself the question why i´m not just gardener or something haha
Thank you man, you're still saving lives out here!
Thank you so much Dr. Todd!
Great demo. Thanks.
Good video by Dr. Grande thanks a lot
this is a good vid.thanks
I'd never thought I'd found a video about kmeans from Dr. Grande, wow.
muchas gracias me fue muy util
This is a very good video, please Dr do also have SOM clustering analysis in SPSS?
Thank you sir for the video.
Hi, can you interpret what these clusters mean for the example ID's? so for example the second cluster of patients have very high dep and sub. use , but score low in anxiety? is this correct? thanks Dr. Todd
Dr great job done
This example was outstanding. I have DV three subscales that I have standardized: Self Esteem, Leadership Efficacy, and Leadership Activism. I have one Categorical IV: Type of mentoring Program: A) One-on-One, B) Team Mentoring, C) Online mentoring, D)Group Mentoring. I'm interested in knowing where these scores (low-high) fall under each Mentoring Type. Ran the analysis. How do I link the cluster to the mentoring type? In the first analysis I used a default cluster, Second analysis, I created 4 groups, which were significant. Should I then create a crosstab of Mentor Type by Group?
good job👍👍👍
Thanks a lot sir! That was helpful !!!!!
You're welcome - thanks for watching.
loved it
thanks a lot
Thanks....
Hi, I was wondering how to read in cluster centers from an external file (after having done the hierarchical clustering) as SPSS always shows error messages (not correct format or one variable name is incorrect). Do you have a video for that? or any solution to my problem?
Thanks for the video! please, how do I combine k-means with mlp in an ensemble form using ibm spss? Also how do I find the ideal cluster number for k means?
So now, which one do we choose as the value of k?
2 or 3?
i never know if this 5 years ago video will helping me now
Great scientists didn't know if they were discovering something big at the beginning
Hello can I get the copy of raw data u used for this tutorial?
Can i proceed to K-means cluster analysis without standarizing the variables?
So what is it, two clusters or three clusters?
can u explain the need to standardise the variables in this case?
it seems they are more or less within the same range, so why the need to standardise?
Dear Dr. Grande. I downloaded SPSS29 but am not able to perform this. The 29th version is totally differe nt.
Thanks for the video. I have two questions:
1- Why did you perform a standardize method at a very early stage?
2- Is the standardize method recommended always or only for some cases?
1. for me this is the case of being able to compare the results; can you imagine interpreting a graph where one variable is between 1 -100, and the second between 20000 - 100000? That'd be very difficult to visualise and interpret. but I'm sure there's a mathematical reason too. 2. I'd go for it every time, but you could probably skip it if your variables are all in the same range (1-100).
By standardizing the score you shift from comparing the values on the original measures to a comparison of cases relative positions on the measures relative to the mean for all cases. You are looking for clusters of cases based on how they relate to the average on the measures. So a cluster with cases that are high on two measures and low on another becomes a cluster that are, for example, at or above average on two measures and below average on the other.
@@MonikaS1890 as op i got a little bit confused as to why standardisation was necessary in this case because it appears all variables Dr Grande is using are within more or less the same range. but i assume he was maybe showing us a more rule of thumb approach or smth like that
It's been 5 years, I've lost all my data science knowledge by now, can't help :D
@@MonikaS1890 😂 that’s okay! thanks anyway!
Someone please tell me why do we use K-Means?
Did you find the answer?. I have the same question
Man, you have to do something with your audio, Don't you listen to your video before uploading??