Cluster analysis of classification is often called the 'non-supervised technique'.
It is a multivariate technique used to determine group membership for cases or variables.
Cluster analysis is a major tool in a number of applications in many fields of Business, Engineering & etc.(The odoridis and Koutroubas, 1999):
Data reduction.
Hypothesis generation.
Hypothesis testing.
Prediction based on groups.
Cluster analysis is a major tool in a number of applications in many fields of Business, Engineering & etc.(The odoridis and Koutroubas, 1999):
Data reduction.
Hypothesis generation.
Hypothesis testing.
Prediction based on groups.
Cluster analysis is a data exploration (mining) tool
for dividing a multivariate dataset into “natural”
clusters (groups). We use the methods to explore
whether previously undefined clusters (groups) may
exist in the dataset.
This presentation educates you about Clustering, Overview, Types of Clustering, Types of clustering algorithms, K-means clustering, Hierarchical clustering, Difference between K Means and Hierarchical clustering and Applications of Clustering.\
For more topics stay tuned with Learnbay.
Cluster analysis is a data exploration (mining) tool
for dividing a multivariate dataset into “natural”
clusters (groups). We use the methods to explore
whether previously undefined clusters (groups) may
exist in the dataset.
This presentation educates you about Clustering, Overview, Types of Clustering, Types of clustering algorithms, K-means clustering, Hierarchical clustering, Difference between K Means and Hierarchical clustering and Applications of Clustering.\
For more topics stay tuned with Learnbay.
This presentation discusses the application of discriminant analysis in sports research. One can understand the steps involved in the analysis and testing its assumptions.
CREDIT RISK MANAGEMENT USING ARTIFICIAL INTELLIGENCE TECHNIQUESijaia
Artificial intelligence techniques are still revealing their pros; however, several fields have benefited from
these techniques. In this study we applied the Decision Tree (DT-CART) method derived from artificial
intelligence techniques to the prediction of the creditworthy of bank customers, for this we used historical
data of bank customers. However we have adopted the flowing process, for this purpose we started with a
data preprocessing in which we clean the data and we deleted all rows with outliers or missing values,
then we fixed the variable to be explained (dependent or Target) and we also thought to eliminate all
explanatory (independent) variables that are not significant using univariate analysis as well as the
correlation matrix, then we applied our CART decision tree method using the SPSS tool.
After completing our process of building our model (DT-CART), we started the process of evaluating and
testing the performance of our model, by which we found that the accuracy and precision of our model is
71%, so we calculated the error ratios, and we found that the error rate equal to 29%, this allowed us to
conclude that our model at a fairly good level in terms of precision, predictability and very precisely in
predicting the solvency of our banking customers.
Introduction to Statistics -
Sampling Techniques, Types of Statistics, Descriptive Statistics,
Inferential Statistics,
Variables and Types of Data: Qualitative, Quantitative, Discrete,
Continuous, Organizing and Graphing Data: Qualitative Data, Quantitative Data
This report contains:-
1. what is data analytics, its usages, its types.
2. Tools used for data analytics
3. description of Classification
4. description of the association
5. description of clustering
6. decision tree, SVM modelling etc with example
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Profile Analysis of Users in Data Analytics DomainDrjabez
Data Analytics and Data Science is in the fast forward
mode recently. We see a lot of companies hiring people for data
analysis and data science, especially in India. Also, many
recruiting firms use stackoverflow to fish their potential
candidates. The industry has also started to recruit people based
on the shapes of expertise. Expertise of a personal is
metaphorically outlined by shapes of letters like I, T, M and
hyphen betting on her experiencein a section (depth) and
therefore the variety of areas of interest (width).This proposal
builds upon the work of mining shapes of user expertise in a
typical online social Question and Answer (Q&A) community
where expert users often answer questions posed by other
users.We have dealt with the temporal analysis of the expertise
among the Q&A community users in terms how the user/ expert
have evolved over time.
Keywords— Shapes of expertise, Graph communities, Expertise
evolution, Q&A community
CLUSTERING DICHOTOMOUS DATA FOR HEALTH CAREijistjournal
Dichotomous data is a type of categorical data, which is binary with categories zero and one. Health care data is one of the heavily used categorical data. Binary data are the simplest form of data used for heath care databases in which close ended questions can be used; it is very efficient based on computational efficiency and memory capacity to represent categorical type data. Clustering health care or medical data is very tedious due to its complex data representation models, high dimensionality and data sparsity. In this paper, clustering is performed after transforming the dichotomous data into real by wiener transformation. The proposed algorithm can be usable for determining the correlation of the health disorders and symptoms observed in large medical and health binary databases. Computational results show that the clustering based on Wiener transformation is very efficient in terms of objectivity and subjectivity.
CLUSTERING DICHOTOMOUS DATA FOR HEALTH CAREijistjournal
Dichotomous data is a type of categorical data, which is binary with categories zero and one. Health care data is one of the heavily used categorical data. Binary data are the simplest form of data used for heath care databases in which close ended questions can be used; it is very efficient based on computational efficiency and memory capacity to represent categorical type data. Clustering health care or medical data is very tedious due to its complex data representation models, high dimensionality and data sparsity. In this paper, clustering is performed after transforming the dichotomous data into real by wiener transformation. The proposed algorithm can be usable for determining the correlation of the health disorders and symptoms observed in large medical and health binary databases. Computational results show that the clustering based on Wiener transformation is very efficient in terms of objectivity and subjectivity.
Similar to Marketing analytics - clustering Types (20)
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When a new product is introduced in market, these analysis will be made.
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SMM Cheap - No. 1 SMM panel in the worldsmmpanel567
Boost your social media marketing with our SMM Panel services offering SMM Cheap services! Get cost-effective services for your business and increase followers, likes, and engagement across all social media platforms. Get affordable services perfect for businesses and influencers looking to increase their social proof. See how cheap SMM strategies can help improve your social media presence and be a pro at the social media game.
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For example, the words, ‘running shoes’ are searched more often than ‘best road running
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These words or phrases which people use to search on Google are called Keywords.
Some keywords are searched more often than others. Number of times a keyword is searched
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Some keywords have more relevant results than others. For the phrase “running shoes” we
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subscription or a premium subscription to the services you offer or a purchase of products
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2. A group of customers are formed based on their
identical characteristics.
This group is formed using the cluster analysis
technique, which is further used for the decision
making.
Segmentation and classification is made from SPSS
17.0.
3. Unsupervised techniques for clustering cases (or
variables) into small number of groups, each having
similar characteristics based on variables (or cases).
Supervised techniques for classifying cases into a group
of defined categories of a response variable of interest
using a set of independent variables (inputs). Various
supervised classification techniques are such as
discriminate analysis, tree modeling, neural network,
etc.,
4. Cluster analysis is often called the 'non-supervised
technique'.
It is a multivariate technique used to determine group
membership for cases or variables.
SPSS provides hierarchical cluster analysis and k-means
cluster analysis.
5. Hierarchical Cluster Analysis: This is used to cluster
variables (or cases). One can analyze raw variables or
use a variety of standardization to transform the
variables.
K-Means Cluster Analysis: This is used to cluster cases
when you have a large number of cases. The analysis
requires one to specify the number of clusters.
Two-Step Cluster Analysis: It is more of a tool than
single analysis. It identifies the grouping by running
pre clustering first and then by hierarchical method
6.
7. LEARNING:
The data about, level of difficulty faculty have on class
to use technology for teaching been collected in form
of questionnaire from.
These details are used for the grouping the faculty using
clustering.
ANALYZE:
The variables considered for the analysis are level of
difficulty faced by faculty when using information
technology in class.
Number of clusters: 2
10. INTERPRETATION/INFERENCE:
From the table ‘Cluster membership’, we were able to
find out that from the variable Q31-A1 till Q31-A8 falls
into one cluster (Cluster1)
And that from the variable Q31-A9 till Q31-A12 falls into
another cluster (Cluster2)
The above ‘Dendogram’ diagram provides the visual
representation of the statistics in clustering solution.
It forms the two clusters that are homogenous in their
characters.
11. CONCLUSION:
Considering the cluster1 which is having the variables from
A1 – A8, whom are the faculty lists who are adaptable to
the technology.
While the other cluster groups Cluster2 (A9 – A12) are formed
in homogeneity where these groups are not willing to get
adapted to the technology, these groups find more difficult
to use technology in class.
From the clusters, we are able to label the group as ‘Stone
Age-Non Versatile’ and ‘Tech Age- Versatile’ faculty
respectively.
12.
13. LEARNING:
The banking customer’s psychographic and demographic
data was collected in form of questionnaire from.
These details are used for the profiling of the customers,
for the purchase of banking products using clustering.
ANALYZE:
The variables considered for the analysis are Age group,
Income group, Perception of living, Knowledge about
schemes and Application of Insurance.
Number of clusters: 5
15. INTERPRETATION/INFERENCE:
Considering the cluster1 which is having the customers age
“31 to 40”, income group “10000 to 20000”, perception
of living as “Costly” and knowledge about the scheme as
“Fully”.
Also the cluster5 which is having the customers age “51 to
60”, income group “NIL”, perception of living as “Costly”
and knowledge about the scheme as “Partially”.
Both these clusters are willing to apply for the insurances and
these clusters will be our target group. The insurance
product will make a good reach over these clusters.
16. Other cluster groups Cluster2, cluster3 and cluster4
are formed in homogeneity where these groups are not
willing to purchase the products, these groups can be
made idle under market promotions.
17. CONCLUSION:
The below mentioned two cluster group applies for the insurance.
From the clusters, we are able to label the group as ‘Middle aged
fiscally challenged’ and ‘Older population fiscally fit’
respectively.