## 4 Research Methodology
### Clustering
#### 4.4.4 Three Layers Proposed Filtering Pre-Preprocess  
Preprocessing is one of the significant and precondition steps in data mining. Feature selection (FS) is a procedure for excellent features that are more useful, but some features may be repetitive, and others may be unrelated and noisy. When the data set contains pointless data, the preprocessing of the dataset is compulsory. Preprocessing step includes:  
1. **Data Cleaning**: Management of lost principles by overlooking tuple, satisfying value with some exact data and supervising noise using discarding methods, clustering, collective human & machine review and regression.
2. Data Integration: Occasionally it has data from several bases in data warehouse and may need to merge them for extra examination. Plan incorporation and redundancy are the main problems in the data incorporation.
3. Data Transformation: Data Transformation is transforming the data in each format to the required format for data mining. Normalization, smoothing, aggregation and generalization are some examples of transformation.
4. Data Reduction: Data analysis on a large number of data takings a long time. It can be achieved by using data cube aggregation, dimension reduction, data compression, numerosity reduction, discretization and concept hierarchy generation.  
For the first three conducts of preprocessing, a \"filter\" selection is in WEKA. In filter selection, there are two categories of filters: Supervised and unsupervised. In both groups, filters are for attributes and instances distinctly. After data cleaning, integration and transformation the data reduction is achieved to get the task applicable data. For data reduction, an \"Attribute Selection\" option is available. It contains several kinds of feature selection programs for wrapper method, filter method and embedded method. Using attribute and instance filters, all attributes and instances can be increased, eliminated, and changed respectively. In this study, a special pre-processing is applied by experts. Three layers filtering Pre-process is applied to the dataset to make imbalanced data balanced. This procedure is used in three steps as follows:  
* **Step A**: Discretization (unsupervised attribute filter): According to the points, for orderly arrangement, this step should be done. Discretization converts one form of data to another form. There are many methods used to describe these two data types, such as 'quantitative' vs. 'qualitative', 'continuous' vs. 'discrete', 'ordinal' vs. 'nominal', and 'numeric' vs. 'categorical'. It is so important to select the most appropriate method for discretization. We classified data in to quantitative or qualitative [53].
* **Step B**: Stratified Remove Folds (supervised instance filter): In our specific data set, this filter plays a vital role in increasing the accuracy of all algorithms. This filter takes a data set and outputs an identified fold for cross-validation.
* **Step C**: Attribute Selection (supervised attribute filter): In order to choose the best attributes for determining the best scenario, this step can be applied.  
The classes to clusters evaluation in WEKA utilizing the foremost assessor to each output of steps described above. It is applied because it is the individual clustering surveyor which yields numeric accuracy as a principle of assessment within several algorithms. The clustering accuracy outcomesencompass true positive values and true negative values. The clustering performance or accuracy considered by the following formulation:  
\[\frac{Truepositive+Truenegative}{Truepositive+Truenegative+Falsepositive+False negative} \tag{65}\]  
Therefore, a true negative is an outcome where the model properly forecasts the negative class. A false positive is an outcome where the model mistakenly forecasts the positive class. And, a false negative is an outcome where the model incorrectly predicts the negative class.