A confusion Knowledge Discovery or Data Mining
Although there have been many practical methods developed and used in data mining, the distinction between data mining and knowledge discovery concepts are not clear yet.
The most critical starting point to extinguish this confusion is to summarize the basic concepts about data mining and knowledge discovery.
--Knowledge discovery is a non-trivial process for identifying valid, new, potentially useful and ultimately understandable patterns in data which consists of nine steps while data mining is the 7 th of those steps.
The above-mentioned 9 steps are as follows;
1. Development and understanding of the application domain
2. Creating a target data set: select the data set, or focusing on a set of variables or data samples on which the discovery was made.3. Data cleaning and preprocessing. transforming raw data into an understandable format. Real-world data is often incomplete, inconsistent, and/or lacking in certain behaviors or trends, and is likely to contain many errors.
4. Data reduction and projection: finding useful features to represent the data depending on the purpose of the task. Through dimensionality reduction methods or conversion, the effective number of variables under consideration may be reduced, or invariant representations for the data can be found.
5. Matching process objectives: KDD with a method of mining particular. For example, summarization, classification, regression, clustering and others.
6. Modeling and exploratory analysis and hypothesis selection: choosing the algorithms or data mining, and select the method or methods to be used in the search for patterns of data. This process includes deciding which model and parameters may be appropriate
7. Data Mining: the search for patterns of interest in a particular representational form or a set of these representations, including classification rules or trees, regression and clustering. The user can significantly aid the data mining method to properly carry out the preceding steps.
8. Interpreting mined patterns, possibly returning to some of the steps between step 1 and 7 for additional iterations. This step may also involve the visualization of the extracted patterns and models or visualization of the data given the models drawn.
9. Acting on the discovered knowledge: using the knowledge directly, incorporating the knowledge in another system for further action, or simply documented and reported to stakeholders.
http://books.google.com/books?id=alHIsT6LBl0C&pg=PA1161&lpg=PA1161&dq=what+is+the+difference+between+data+mining+and+knowledge+discovery&source=bl&ots=pqHBwbAOjv&sig=RkfNlkC8sqoJDfjoFOo-SfdG_kE&hl=en&sa=X&ei=EDRiUZvdBoi88AT05oH4CQ&ved=0CGAQ6AEwBQ#v=onepage&q=what%20is%20the%20difference%20between%20data%20mining%20and%20knowledge%20discovery&f=false
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1547224
http://smartdatacollective.com/josueoteiza/38043/difference-between-knowledge-discovery-and-data-mining