Association Rule

Apriori Algorithm

Attempts to find subsets that are common to at least a minimum number c (cutoff, or confidence threshold) of the itemsets.

What does apriori algorithm discover?

Association rules

Who discovered the apriori algorithm?

Agrawal and Srikant in 1994

What are the Steps of Apriori?

Generate 1-itemset frequent pattern based on a defined minimum support value. this set is denoted L1, L1 is used to find L2, the set of frequent 2-itemsets, which is used to find L3, and Repeat until no more frequent k-itemsets can be found

What is this graphic an example of?

The Apriori Algorithm

Market basket analysis

Is a common analysis running against a transaction database to find sets of items, or item sets, that appear together in many transactions.

Examples of application of association rule mining.

Improve the placement of items in a store, promote the items as a package, the layout of web pages, product recommendations

What are the pros of apriori?

Easy-to-implement, easy-to-understand and can be used on large itemsets.

What are the cons of apriori?

May need to find a large number of candidate rules that can be computationally expensive and calculating support is also expensive because it has to go through the entire database.

Sequential Patter Analysis

Similar to association rule mining, except that the relationship exists over a period of time -> where one event leads to another later event(time-series data analysis).

Examples of sequential pattern analysis.

After inflate rate increases, the stock market is likely to go down within a week and after a student takes statistics course, what course is most likely to be next in the following semester?

Examples of Business Management Issues.

"We have mountains of data in this company, but we can't access it " , "We may need to slice and dice the data in every way" , "You've got to make it easy for business people to get at the data directly"

Data Warehouse

A subject-oriented, integrated, time-variant, and nonvolatile collection of data in support of management's decision-making process.

A ________ is a repository of consolidated multiple heterogeneous data sources.

data warehouse

Data Warehouses can be _____ and maintained ______ from _____ databased. Organized under a unified scheme at a single site in order to facilitate decision making.

different, separately, operational

What does the data warehouse facilitate?

Decision making(analysis of data for decision makers)

What does data warehouse not facilitate?

Daily operations or transaction processing.

Benefits of Data Warehouse.

Fast access data/info on a single site

Data Warehouses are usually archive in different databases at _______ locations (distributed).

different

Short-term Goals of Data Warehouse.

Improve data quality, minimize inconsistent reports, provide data sharing, integrate data from multiple sources, merge historical and current data appropriately, and improving the speed and performance of reporting.

Long-term goals of Data Warehouse.

Provide a consolidated view of enterprise data and develop an enterprise approach to business intelligence and decision support.

What are characteristics of data warehouse?

Subject oriented, integrated, time-variant, nonvolatile.

What does it mean for a data warehouse to be subject oriented?

Data are organized based on major subject areas of the corporation.

What does it mean for a data warehouse to be integrated?

data in a DW are collected from distributed, heterogeneous data sources.

What does it mean for a data warehouse to be time-variant?

Data has a time dimension - each data point is associated with a point in time.

What does it mean for a data warehouse to be nonvolatile?

New data is always appended rather than replaced. The database continually absorbs new data, integrating it with the previous data.

What is the name of Data Staging Area

Extraction, Transformation, Loading (ETL)

Extraction

reading and understanding the source data and copying the data needed for the data warehouse into the staging area for further manipulation.

Transformation

cleansing, combining data from multiple sources, deduplicating data, and assigning warehouse keys

Loading

loading the data into the data warehouse presentation area

What are data access tools?

tools that query the data in the data warehouse’s presentation area.

What is the purpose of data access tools?

the variety of capabilities that can be provided to business users to leverage the presentation area for analytic decision making

Examples of Data Access Tools.

prebuilt parameter-driven analytic applications, ad hoc query tools, data mining, modeling, forecasting

Data Warehouse are very costly and limit their used to large companies.(T/F)

True

________: data about spanning the whole organization.

Data Warehouse

Datamarts

A lower-cost, scaled down version of a data warehouse and specialized for a single department.

How to create a datamart?

Replicated functional subsets of the dataware.S

Standalone data marts

a company may have several independent data marts without a DW.

What type of data model is used for a data warehouse?

A multidimensional structure

How is the data model for a data warehouse structured?

Each dimension corresponds to an attribute or a set of attributes in the schema.

What are the characteristics of a Star Schema?

Uses fact and dimension tables, each dimension is represented by a dimension-table, transactions are described through a fact table, easy for users to understand and optimized for OLAP.

What is the OLAP(Online Analytical Processing) used for?

To enable the user to gain insight into data through interactive access to a wide variety of possible views of the information

How can you view data in OLAP?

From different perspectives and different levels of abstractions(Grain).

What 2 analyses are used for OLAP?

Drill-down and Roll-up

Drill-down

Takes you all the way down to the transaction detail level.

Roll-up

Is the reverse of drill-down.

What does drilling down in OLAP help you discover?

Trends

What 2 view perspectives are used to alter dimensions in OLAP?

Slicing and Dicing

Slicing

taking out the slice of a cube, given certain set of select dimension (customer segment), and value (home furnishings..) and measures (sales revenue, sales units..) or KPIs (Sales Productivity).

Dicing

viewing the slices from different angles and allowing decision support system users to change their view perspective.