Data Mining

PAPER   PRESENTATION ON
DATA MINING











INDEX


1 DATA MINING


  1) INTRODUCTION


  2) KNOWLEDGE DISCOVERY DATABASE [KDD]


  3) REPORTING AND DISPLAY OF DISCOVERED INFORMATION


  4) TEN GOLDEN RULES IN DATA MINING


  5) APPLICATIONS OF DATA MINING


  6) TEXT MINING


  7) MINING ASSOCIATION RULES


                                      ALGORITHM
                                                        CONCLUSION








DATA MINING

1) INTRODUCTION


        Data Mining is the actual phase of a knowledge discovery process. It is one of the motivations of machine learning. This is the explosion of data in modern society, and the corollary that mechanical production of data has created the need for mechanical consumption of data. Most organizations have large databases that contain a wealth of potentially accessible information. However, it is usually very difficult to access this information. The UN bridled growth of data will inevitably lead to a situation in which it is increasingly difficult to access the desired information; it will always be looking for a needle in a haystack, only the amount of hay will be growing all the time.


                The great interest that is being shown in the new field of 'Data Mining' or KDD (Knowledge Discovery in Databases) is understandable. The first international conference was held in Montreal in1995, it was proposed that the term 'KDD' be employed to describe the whole process of extraction and patterns between data elements. It was further proposed that the term ‘data mining’ should be used exclusively for the discovery stage of the KDD process.















































2) KNOWLEDGE DISCOVERY DATABASE [KDD]


Data mining as part of knowledge discovery process

✓ Data selection: - data about specific categories
✓ Data cleaning: - corrects invalid items and eliminate...