- Agglomerative Hierarchical clustering method allows the clusters to be read from bottom to top and it follows this approach so that the program always reads from the sub-component first then moves to the parent. Whereas, divisive uses top-bottom approach in which the parent is visited first then the child. 

- Agglomerative hierarchical method consists of objects in which each object creates its own clusters and these clusters are grouped together to create a large cluster. It defines a process of merging that carries on till all the single clusters are merged together into a complete big cluster that will consists of all the objects of child clusters. Whereas, in divisive the parent cluster is divided into smaller cluster and it keeps on dividing till each cluster has a single object to represent.

There are many algorithms that can be used to analyze the database to check the maintenance of all the data sets that are already present. The different types of cluster models include as follows:

- Connectivity models: these are the models that connect one cluster to another cluster. This includes the example of hierarchical clustering that is based on the distance connectivity of one model to another model. 

- Centroid models: these are the models that are used to find the clusters using the single mean vector. It includes the example of k-means algorithm.

- Distribution models: it includes the specification of the models that are statistically distributed for example multivariate normal distribution model.

- Density models: deals with the clusters that are densely connected with one another in the regions having the data space. 

- Group models: specifies the model that doesn’t provide the refined model for the output and just gives the grouping information

Cluster analysis is used to define the object without giving the class label. It analyzes all the data that is present in the data warehouse and compare the cluster with the cluster that is already running. It performs the task of assigning some set of objects into the groups are also known as clusters. It is used to perform the data mining job using the technique like statistical data analysis. It includes all the information and knowledge around many fields like machine learning, pattern recognition, image analysis and bio-informatics. Cluster analysis performs the iterative process of knowledge discovery and includes trials and failures. It is used with the pre-processing and other parameters as a result to achieve the properties that are desired to be used.

Bitmap indexes make use of bit arrays (bitmaps) to answer queries by performing bitwise logical operations.

Bitmap indexes are useful in the data warehousing applications.

Bitmap indexes have a significant space and performance advantage over other structures for such data.

Tables that have less number of insert or update operations can be good candidates.

The advantages of Bitmap indexes are:

   - They have a highly compressed structure, making them fast to read.

   - Their structure makes it possible for the system to combine multiple indexes together so that they can      access the underlying table faster.

The Disadvantage of Bitmap indexes is:

  - The overhead on maintaining them is enormous. 

Data cleaning is also known as data scrubbing. Data cleaning is a process which ensures the set of data is correct and accurate. Data accuracy and consistency, data integration is checked during data cleaning. Data cleaning can be applied for a set of records or multiple sets of data which need to be merged.

Data cleaning is performed by reading all records in a set and verifying their accuracy. Typos and spelling errors are rectified. Mislabeled data if available is labeled and filed. Incomplete or missing entries are completed. Unrecoverable records are purged, for not to take space and inefficient operations.

At the time of updating the data warehouse, a lookup table is used. When placed on the fact table or warehouse based upon the primary key of the target, the update is takes place only by allowing new records or updated records depending upon the condition of lookup.

The materialized views are aggregate tables. It contains summarized data. For example, to generate sales reports on weekly or monthly or yearly basis instead of daily basis of an application, the date values are aggregated into week values, week values are aggregated into month values and month values into year values. To perform this process aggregate function is used.

The primary keys of entity tables are the foreign keys of dimension tables.

The Primary keys of fact dimensional table are the foreign keys of fact tablels.

Data Mart is a data repository which is served to a community of people who works on knowledge (also known as knowledge workers). The data resource can be from enterprise resources or from a data warehouse.