.. this is api API === .. *Optimization: Still more room for improvement* -vy **Summary** Simply put,`kClusterLib` is a python tool to perform `clustering `_ of sensors using unprocessed/raw sensor data. It uses the famous `pyCluster `_ clustering library. **Introduction** ---------------- | For quick tryout of this work jump to `usage `_ document. This library is composed of multiple modules kept together inside a single directory `kClusterLib `_. **Background** Clustering is essentially an optimization process that tries to group similar sensors/objects based on **degree of similarity** in their(sensor's) behavior/output( **correlation** ). The objective function( the parameter being minimized ) is the sum of intra cluster distances i.e. the measure of how close/similar are the element which are grouped together. However, the compactness of clusters(groups) is directly affected by the choice of *number of clusters*. This make the clustering a two dimentional problem. An optimum value for number of clusters is to be determined and at the same time grouping( clustering ) is to be performed. The optimal clusters are recursively computed and are subject to * Minimizing :math:`S_i = \frac{\displaystyle{\sum_{i=1}^{k}} \text{ (Intra Cluster Distances)}_i}{\displaystyle{\sum_{i=1}^{k}} \text{ (Inter Cluster Distances)}_i}` * Minimizing **k**: Number of clusters **Components** --------------- `api `_ : (current page) Contains brief information of the library work.bout the various parts and pieces of this work. `db connector `_: Database connection object and methods are defined in this module. `kc tools `_: Tools for data pre-processing are to be located here. `filterV `_: Filter transfer function is defined in this module. `usage `_: A demo example. | ... `Read on to learn more `_