The aim of this study is to show the importance of two classification techniques, viz. decision tree and clustering, in prediction of learning disabilities (LD) of school-age children. LDs affect about 10 percent of all children enrolled in schools. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. Decision trees and clustering are powerful and popular tools used for classification and prediction in Data mining. Different rules extracted from the decision tree are used for prediction of learning disabilities. Clustering is the assignment of a set of observations into subsets, called clusters, which are useful in finding the different signs and symptoms (attributes) present in the LD affected child. In this paper, J48 algorithm is used for constructing the decision tree and K-means algorithm is used for creating the clusters. By applying these classification techniques, LD in any child can be identified.
Data mining is a collection of techniques for efficient automated discovery of previously unknown, valid, novel, useful and understandable patterns in large databases. Conventionally, the information that is mined is denoted as a model of the semantic structure of the datasets. The model might be utilized for prediction and categorization of new data [1]. In recent years the sizes of databases has increased rapidly. This has lead to a growing interest in the development of tools capable in the automatic extraction of knowledge from data. The term Data Mining or Knowledge Discovery in databases has been adopted for a field of research dealing with the automatic discovery of implicit information or knowledge within databases [16]. Diverse fields such as marketing, customer relationship management, engineering, medicine, crime analysis, expert prediction, web mining and mobile computing besides others utilize data mining [7].
Databases are rich with hidden information, which can be used for intelligent decision making. Classification and prediction are two forms of data analysis that can be used to extract models describing important data classes or to predict future data trends [8]. Classification is a data mining (machine learning) technique used to predict group membership for data instances. Machine learning refers to a system that has the capability to automatically learn knowledge from experience and other ways [4]. Classification predicts categorical labels whereas prediction models continuous valued functions. Classification is the task of generalizing known structure to apply to new data while clustering is the task of discovering groups and structures in the data that are in some way or another similar, without using known structures in the data. Decision trees are supervised algorithms which recursively partition the data based on its attributes, until some stopping condition is reached [8]. This recursive partitioning, gives rise to a tree-like structure. Decision trees are white boxes as the classification rules learned by them can be easily obtained by tracing the path from the root node to each leaf node in the tree. Decision trees are very efficient even with the large volumes data. This is due to the partitioning nature of the algorithm, each time working on smaller and smaller pieces of the dataset and the fact that they usually only work with simple attribute-value data which is easy to manipulate. The Decision Tree Classifier (DTC) is one of the possible approaches to multistage decision-making. The most important feature of DTCs is their capability to break down a complex decision making process into a collection of simpler decisions, thus providing a solution, which is often easier to interpret [17].
Clustering is the one of the major data mining tasks and aims at grouping the data objects into meaningful classes or clusters such that the similarity of objects within clusters is maximized and the similarity of objects from different clusters is minimized [10]. Clustering separates data into groups whose members belong together. Each object is assigned to the group it is most similar to. Cluster analysis is a good way for quick review of data, especially if the objects are classified into many groups. Clustering does not require a prior knowledge of the groups that are formed and the members who must belong to it. Clustering is an unsupervised algorithm [6]. Clustering is often confused with classification, but there is some difference between the two. In classification the objects are assigned to pre defined classes, whereas in clustering the classes are also to be defined [11].
LD is a neurological condition that affects a child’s brain and impairs his ability to carry out one or many specific tasks. These like children are neither slow nor mentally retarded. An affected child can have normal or above average intelligence. This is why a child with a learning disability is often wrongly labeled as being smart but lazy. LDs affect about 10 percent of all children enrolled in schools. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. Pediatricians are often called on to diagnose specific learning disabilities in school-age children. Learning disabilities affect children both academically and socially. These may be detected only after a child begins school and faces difficulties in acquiring basic academic skills [11]. Learning disability is a general term that describes specific kinds of learning problems. Specific learning disabilities have been recognized in some countries for much of the 20 th century, in other countries only in the latter half of the century, and yet not at all in other places [11]. A learning disability can cause a person to have trouble learning and using certain skills. The skills most often affected are: reading, writing, listening, speaking, reasoning, and doing math. If a child has u
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