Knowledge discovery as a process consists of an iterative sequence of the following steps:

1. Data cleaning:

 It can be applied to remove noise and correct inconsistencies in the data.

1. Data integration:

 Data integration merges data from multiple sources into a coherent data store, such as a data warehouse.

1. Data selection:

 where data relevant to the analysis task are retrieved from the database.

1. Data transformation:

 where data are transformed or consolidated into forms appropriate for mining by performing summary or aggregation operations.

 example, normalization may improve the accuracy and efficiency of mining algorithms involving distance measurements.

1. Data mining:

 an essential process where intelligent methods are applied in order to extract data patterns.

1. Pattern evaluation:

 to identify the truly interesting patterns representing knowledge based on some interestingness measures.

1. Knowledge presentation:

 where visualization and knowledge representation techniques are used to present the mined knowledge to the user.

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