the data, where each partition represents a cluster and it classifies the data into K groups. Each group contains at least one object and each object must belong to exactly one group. The clustering results obtained by us are shown under: Clustered Instances LD = 0 (No) - 94 Nos. - 75.20 % Clustered Instances LD = 1 (Yes) - 31 Nos. - 24.80 % The clustering history and the cluster visulizer, indicating LD = Y and LD = N are as shown in Table 3 and in Figure 2 respectively below. 

| Sl. No   | Attributes                                      | Full Data (125)   | LD = 0 (No)   | LD = 1 (Yes)   |
|----------|-------------------------------------------------|-------------------|---------------|----------------|
| 1        | DR                                              | 50.224            | 48.787        | 54.581         |
| 2        | DS                                              | 6.408             | 6.383         | 6.484          |
| 3        | DH                                              | 30.744            | 30.596        | 31.194         |
| 4        | DWE                                             | 85.552            | 86.351        | 83.129         |
| 5        | DBA                                             | 85.008            | 86.511        | 80.452         |
| 6        | DHA                                             | 79.712            | 81.011        | 75.774         |
| 7        | DA                                              | 67.312            | 67.734        | 66.032         |
| 8        | ED                                              | 83.960            | 85.319        | 79.839         |
| 9        | DM                                              | 85.240            | 86.245        | 82.194         |
| 10       | LM                                              | 82.176            | 80.766        | 86.452         |
| 11       | DSS                                             | 84.632            | 81.436        | 94.323         |
| 12       | DNS                                             | 81.344            | 78.787        | 89.097         |
| 13       | DLL                                             | 83.400            | 81.457        | 89.290         |
| 14       | DLS                                             | 86.200            | 82.521        | 97.355         |
| 15       | STL                                             | 83.632            | 83.117        | 85.194         |
| 16       | RG                                              | 85.248            | 84.692        | 86.936         |
|          | No. of iterations                               | 2                 |               |                |
|          | Within cluster sum of squared errors            | 86.105            |               |                |
|          | Missing values globally replaced with mean/mode |                   |               |                |

## Figure 2. Cluster Visulizer 

![0_Image_0.Png](0_Image_0.Png) 4. Result Analysis

In this study, we are used 125 real data sets with 16 attributes most of which takes binary values for the LD classifications. J48 algorithms are found very suitable for handling missing values and the key symptoms of LD can easily be predicted. The decision tree is very user friendly architecture compared to other classification methods. J48 decision tree is better in terms of efficiency and complexity. From this study, we have obtained that; decision tree correctly 