- Choose File → Options
- Select the Add-Ins option on the left, and select COM Add-Ins from the Manage drop-down menu
- Select SQLServer
What are the 6 processes of data mining
Data mining is as much analytical process as it is specific algorithms and models.
Like the CIA Intelligence Process, the CRISP-DM process model has been broken down into six steps: business understanding, data understanding, data preparation, modeling, evaluation, and deployment.
How do you perform market basket analysis using Association rules apriori
APRIORI Algorithm Association Rule Mining is viewed as a two-step approach: Frequent itemset generation: Find all frequent item-sets with support >= pre-determined min_support count.
Rule Generation: List all Association Rules from frequent item-sets. Calculate Support and Confidence for all rules.
What is Apriori algorithm in data mining
Apriori is an algorithm for frequent item set mining and association rule learning over relational databases.
It proceeds by identifying the frequent individual items in the database and extending them to larger and larger item sets as long as those item sets appear sufficiently often in the database.
What are different types of analytics?
- Descriptive Analytics
- Diagnostic Analytics
- Predictive Analytics
- Prescriptive Analytics
What is association analysis
Association analysis is the task of finding interesting relationships in large datasets. These interesting relationships can take two forms: frequent item sets or association rules.
Frequent item sets are a collection of items that frequently occur together.
What are the advantages of market-based transfer pricing
In summary, the main advantage of market-based transfer prices is that they are objective and unbiased measures, although they might fluctuate because of mar- ket conditions over time.
Further, they are difficult to manipulate.
What is the meaning of market basket
A market basket refers to a selection of goods and services that are consistently purchased and sold throughout an economic system.
Economists, politicians, and financial analysts use market baskets to track price changes over time and determine inflation levels.
Which analytical technique is best suited for cross selling or finding next best product offers
This is called Market Basket Analysis (also called MBA). It is a widely used technique to identify the best possible mix of frequently bought products or services.
This is also called product association analysis.
What is two way lift in market basket analysis
Lift can be calculated as two-way (for identifying association of 2 products), three-way (for identifying association of 3 products).
Above table shows that baby product and DVDs have highest lift.
What is outliers in data mining
Outlier is a data object that deviates significantly from the rest of the data objects and behaves in a different manner.
An outlier is an object that deviates significantly from the rest of the objects.
They can be caused by measurement or execution errors.
What is the difference between classification and clustering in data mining
Differences between Classification and Clustering The process of classifying the input instances based on their corresponding class labels is known as classification whereas grouping the instances based on their similarity without the help of class labels is known as clustering.
What does an affinity analysis do
Affinity Analysis is the kind of predictive analysis technique that does the process of data mining and fetches the hiding insightful correlation between the different variables based on their co-occurrence happening in between the individuals or the groups in the dataset.
How do you calculate lift in data mining
Lift can be found by dividing the confidence by the unconditional probability of the consequent, or by dividing the support by the probability of the antecedent times the probability of the consequent, so: The lift for Rule 1 is (3/4)/(4/7) = (3*7)/(4 * 4) = 21/16 ≈ 1.31.
What is clickstream analysis
A form of Web analytics (see separate entry), clickstream analysis is the tracking and analysis of visits to websites.
Although there are other ways to collect this data, clickstream analysis typically uses the Web server log files to monitor and measure website activity.
What is Apriori analysis
Apriori analysis means, analysis is performed prior to running it on a specific system.
This analysis is a stage where a function is defined using some theoretical model.
How does cross selling promote customer service
Cross-selling involves selling related, supplementary products or services based on the customer’s interest in, or purchase of, one of your company’s products.
Its a great way of increasing customer loyalty and deeping customer relationships which in turn can improve customer lifetime value and retention.
What is the difference between absolute and relative support in data mining
Support of an Item Set Then the absolute support (or simply the support) of the item set S is the number of transactions in T that contain S. Likewise, the relative support of S is the fraction (or percentage) of the transactions in T which contain S.
What are advantages of Apriori algorithm
The Apriori algorithm advantages are as follows: This is the most simple and easy-to-understand algorithm among association rule learning algorithms.
The resulting rules are intuitive and easy to communicate to an end-user.
Why is it called Apriori algorithm
Name of the algorithm is Apriori because it uses prior knowledge of frequent itemset properties.
We apply an iterative approach or level-wise search where k-frequent itemsets are used to find k+1 itemsets.
What are clustering methods
Clustering methods are used to identify groups of similar objects in a multivariate data sets collected from fields such as marketing, bio-medical and geo-spatial.
They are different types of clustering methods, including: Partitioning methods. Hierarchical clustering. Fuzzy clustering.
What is Apriori algorithm with example
Apriori algorithm refers to an algorithm that is used in mining frequent products sets and relevant association rules.
Generally, the apriori algorithm operates on a database containing a huge number of transactions.
For example, the items customers but at a Big Bazar.
What are the two principles of Apriori algorithm
Apriori algorithm was the first algorithm that was proposed for frequent itemset mining. It was later improved by R Agarwal and R Srikant and came to be known as Apriori.
This algorithm uses two steps “join” and “prune” to reduce the search space.
What is disadvantage of Apriori algorithm
The major drawback with Apriori algorithm is of time and space. It generates numerous uninteresting itemsets which lead to generate various rules which are of completely of no use.
The two factors considered for association rules generation are Minimum Support Threshold and Minimum Confidence Threshold.
What is the difference between lift and leverage
The lift calculates the ratio of these factors, i.e ( formula) and leverage calculates the difference, i.e (formula) The implications are that lift may find very strong associations for less frequent items, while leverage tends to prioritize items with higher frequencies/support in the dataset.
Why clustering is used
Clustering is used to identify groups of similar objects in datasets with two or more variable quantities.
In practice, this data may be collected from marketing, biomedical, or geospatial databases, among many other places.
What is the lift formula
The modern lift equation states that lift is equal to the lift coefficient (Cl) times the density of the air (r) times half of the square of the velocity (V) times the wing area (A).
What are the two basic steps in Apriori algorithm
Apriori algorithm was the first algorithm that was proposed for frequent itemset mining. It was later improved by R Agarwal and R Srikant and came to be known as Apriori.
This algorithm uses two steps “join” and “prune” to reduce the search space. It is an iterative approach to discover the most frequent itemsets.
What is an example of clustering
Retail companies often use clustering to identify groups of households that are similar to each other.
For example, a retail company may collect the following information on households: Household income.
Household size.
Is clustering supervised or unsupervised
Unlike supervised methods, clustering is an unsupervised method that works on datasets in which there is no outcome (target) variable nor is anything known about the relationship between the observations, that is, unlabeled data.
Sources
https://en.wikipedia.org/wiki/Market_intervention
https://www.statology.org/cluster-analysis-real-life-examples/
https://www.upgrad.com/blog/clustering-and-types-of-clustering-methods/
