Lecture slides Wed - Indiana University Computer Science
... – Approach: • Collect different attributes of customers based on their geographical and lifestyle related information. • Find clusters of similar customers. • Measure the clustering quality by observing buying patterns of customers in same cluster vs vs. those from different clusters ...
... – Approach: • Collect different attributes of customers based on their geographical and lifestyle related information. • Find clusters of similar customers. • Measure the clustering quality by observing buying patterns of customers in same cluster vs vs. those from different clusters ...
distance based transformation for privacy preserving data
... Cryptographic techniques are applied in distributed environment. Secure Multiparty Computation (SMC) is the well known technique in this category. In SMC two or more parties compute secure sum on their inputs and transfer to the other party without disclosing the original data [10, 11 and ...
... Cryptographic techniques are applied in distributed environment. Secure Multiparty Computation (SMC) is the well known technique in this category. In SMC two or more parties compute secure sum on their inputs and transfer to the other party without disclosing the original data [10, 11 and ...
Computer Science - University of Hyderabad
... study material on your own. The School will not provide the same. These are all selfstudy courses, there will be no classes/tutorials etc. Nevertheless, all candidates regardless of mode (Full-time, Part-time, External) will be free to audit on going courses for MTechs/MCAs. 4. The School faculty wi ...
... study material on your own. The School will not provide the same. These are all selfstudy courses, there will be no classes/tutorials etc. Nevertheless, all candidates regardless of mode (Full-time, Part-time, External) will be free to audit on going courses for MTechs/MCAs. 4. The School faculty wi ...
Optimized Protocol for Privacy Preserving Clustering Miss Mane P.B. Mr Kadam S.R.
... Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). Achieving privacy preservation when sharing data for clustering is a challenging problem. To address this problem, data owners must not only meet privacy requirements but ...
... Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). Achieving privacy preservation when sharing data for clustering is a challenging problem. To address this problem, data owners must not only meet privacy requirements but ...
Design of OLAP Cube for Banking System of India
... SQL command that consisting six tables. Select c. Name, c. Age from customer c. employee e. branch b. time t. account a, trans tr where c. cust_id = tr. cust_id and tr. emp_id = e,emp_id and tr.time_id = t.Time_id and tr.acc_id group by c.Name, c.Age In OLAP large amount of data require many joins f ...
... SQL command that consisting six tables. Select c. Name, c. Age from customer c. employee e. branch b. time t. account a, trans tr where c. cust_id = tr. cust_id and tr. emp_id = e,emp_id and tr.time_id = t.Time_id and tr.acc_id group by c.Name, c.Age In OLAP large amount of data require many joins f ...
Comparative Analysis of EM Clustering Algorithm and Density
... Abstract:- Machine learning is type of artificial intelligence wherein computers make predictions based on data. Clustering is organizing data into clusters or groups such that they have high intra-cluster similarity and low inter cluster similarity. The two clustering algorithms considered are EM a ...
... Abstract:- Machine learning is type of artificial intelligence wherein computers make predictions based on data. Clustering is organizing data into clusters or groups such that they have high intra-cluster similarity and low inter cluster similarity. The two clustering algorithms considered are EM a ...
DATA MINING II
... – Involves the analysis of data and the use of software techniques for finding hidden and unexpected patterns and relationships in sets of data; in contrast to information and knowledge that are already intuitive. – Patterns and relationships are identified by examining the underlying rules an ...
... – Involves the analysis of data and the use of software techniques for finding hidden and unexpected patterns and relationships in sets of data; in contrast to information and knowledge that are already intuitive. – Patterns and relationships are identified by examining the underlying rules an ...
Data Mining Anomaly Detection
... – Compute the distance between every pair of data points – There are various ways to define outliers: Data ...
... – Compute the distance between every pair of data points – There are various ways to define outliers: Data ...
IS 579— Business Intelligence and Data Mining
... Over the years, organizations have accumulated a vast amount of data in their enterprise-wide information systems. These data typically represent daily operations and transactions within a business context. It is easy to see that all the business intelligence and rules are, in some way, embedded in ...
... Over the years, organizations have accumulated a vast amount of data in their enterprise-wide information systems. These data typically represent daily operations and transactions within a business context. It is easy to see that all the business intelligence and rules are, in some way, embedded in ...
Unsupervised Learning: Clustering
... determine input parameters Able to deal with noise and outliers Insensitive to order of input records High dimensionality ...
... determine input parameters Able to deal with noise and outliers Insensitive to order of input records High dimensionality ...
G-DBSCAN: An Improved DBSCAN Clustering Method Based On Grid
... Although DBSCAN algorithm itself can remove noise points, it will also occupy memory space when judging the noise points. This also led to the processing speed of DBSCAN algorithm is slow. In order to improve the processing speed, in view of the above problem, we improved DBSCAN clustering algorithm ...
... Although DBSCAN algorithm itself can remove noise points, it will also occupy memory space when judging the noise points. This also led to the processing speed of DBSCAN algorithm is slow. In order to improve the processing speed, in view of the above problem, we improved DBSCAN clustering algorithm ...
How Data Mining Is Used
... What is needed to do DM? DM requires the identification of a problem, along with data collection that can lead to a better understanding of the market. Computer models provide statistical or other means of analysis. Two general types of DM studies: 1. Hypothesis testing: involving expressing a theor ...
... What is needed to do DM? DM requires the identification of a problem, along with data collection that can lead to a better understanding of the market. Computer models provide statistical or other means of analysis. Two general types of DM studies: 1. Hypothesis testing: involving expressing a theor ...
SPATIO-TEMPORAL PATTERN CLUSTERING METHOD BASED
... considering the time evolution of the scene. Therefore, SITS are high complexity data containing numerous and various spatio-temporal structures. For example in a SITS, growth, maturation or harvest of cultures can be observed. Specialized tools for information extraction in SITS have been made such ...
... considering the time evolution of the scene. Therefore, SITS are high complexity data containing numerous and various spatio-temporal structures. For example in a SITS, growth, maturation or harvest of cultures can be observed. Specialized tools for information extraction in SITS have been made such ...
Nonlinear dimensionality reduction
High-dimensional data, meaning data that requires more than two or three dimensions to represent, can be difficult to interpret. One approach to simplification is to assume that the data of interest lie on an embedded non-linear manifold within the higher-dimensional space. If the manifold is of low enough dimension, the data can be visualised in the low-dimensional space.Below is a summary of some of the important algorithms from the history of manifold learning and nonlinear dimensionality reduction (NLDR). Many of these non-linear dimensionality reduction methods are related to the linear methods listed below. Non-linear methods can be broadly classified into two groups: those that provide a mapping (either from the high-dimensional space to the low-dimensional embedding or vice versa), and those that just give a visualisation. In the context of machine learning, mapping methods may be viewed as a preliminary feature extraction step, after which pattern recognition algorithms are applied. Typically those that just give a visualisation are based on proximity data – that is, distance measurements.