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I have couple queries related my research in areas of data mining and machine learning.

I have software development life cycle data set containing features like:

-project id [alphanumeric]

-client details [alphanumeric]

-technology / domain used for development [alphanumeric]

-planned and actual no.of days spent for project development all phases individually [number]

-planned and actual cost spent for project development all phases individually [currency and number]

-total members of team for all phases individually [number]


1) If this historical data about software project development life cycle is made available in clustered form [groups] do you think it will be useful to handle new upcoming projects easily?

2) If yes in what sense?

I have developed a new statistical algorithm for clustering [rather incremental clustering] only numeric dataset and worked with data from other domains as well. Now the task is to cluster SDLC data. So need to know:
1) How fruitful it is to store SDLC data for forecasting / estimation / incremental learning and knowledge augmentation?
2) And how to convert alphanumeric software project data [as mentioned above] into completely numeric so as to perform statistical computations for clustering. Any suggestions. Thx.

Tags: Incremental, clustering, cycle, data, development, incremental, learning, life, sets, software

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