This essay focuses on preprocessing methods such as data cleansing. classification and clustering are applied to reveal hidden knowledge

preprocessing methods such as data cleansing

, variable transformation, and data partitioning must be used. 3. Data mining. Data mining algorithms, such as classification and clustering, are applied to predict student success. 4. Interpretation. At this stage, the models are analyzed to predict student success.12 Various data mining techniques such as classification and clustering are applied to reveal hidden knowledge from educational data.6 Clustering is used by pattern analysis, decision-making, and machine learning, which includes data mining, document retrieval, image segmentation, and pattern classification.5 Various pieces of information stored for each event can be used for clustering, correlating, and finding causal relationships in the event logs.4

Using cluster analysis,

we separate students into groups, so that students in the same group share the same progression within the group.6 Data clustering used with k-means algorithm enables teachers to predict student performance and associate learning styles of different learner types and their behavior with the aim of collectively improving institutional performance.13 K-means is the most popular and the simplest partitional algorithm used for clustering.14 “Measuring the similarity of two objects is done by calculating a distance measure such as the Euclidean Distance attributes having numerical values.”6 Several methods have been developed to solve classification problems

. Among all these methods,

decision tree is recognize as suitable, because it is consider to be one of the most commonly use methods in the supervise learning approach.15 Decision tree is a classification algorithm that is display in the form of a tree in which two different types of nodes are connect by branches.

3 The induction of the decision tree is do through a supervise knowledge discovery process in which prior class knowledge was use to channel new knowledge.16 The tree consists of internal nodes that match the logical attribute test and the connecting branches which represent the test outcomes.6 The decision tree classifies instances by sorting them down the tree from the root to the leaf nodes.2 The decision tree is consider to be a procedure that

decides whether a particular value will be accept or reject, uses IF-THEN rule, and ensures that the current state is map to a future state to make a different decision.3 IF-THEN rule is one of the most popular forms of knowledge representation because it is easy to understand and interpret by nonexpert users and can be directly apply in the decision-making process.12 The nodes and the branches form a consecutive path through the decision tree that reaches the leaves, and it represents a specific mark. All the nodes in the tree correspond to a subset of data. Ideally, the leaf is clean, which means that all elements in the leaf have an equal chance of being a target variable or a class.6 In the context of learning through the decision tree, the target variables refer to attributes. Each attribute node splits a set of instances into two or more subsets.

The root of the tree corresponds

to all instances.17 Decision trees are easy to understand and well adapted to the classification problems. They suffer from a sensitivity of the data. They are  use in their construction and they are a less natural model for regression. The advantage of decision trees is that there is a large number of efficient algorithms. This is which can find approximately optimal tree architectures.18 In addition, decision trees are able to break down the complex problem of decision-making into several simpler ones.15 The steps in decision tree building are as

follows; 1.Firstly,  Suppose C is a set of objects to

be classified by starting

from the current node.  Otherwise, we move on to step 2. 2. secondly, Suppose Ai is the attribute select for the current node. The attribute Ai has possible values in Vi ¼ fAi1, Ai2, … , Aivg. 3. Thirdly, We use attribute values to divide the set of objects C into mutually exclusive and exhaustive subsets fCi1, Ci2, … , Civg. Each subset of Cij contains objects in C which have the value Aij for the attribute Ai.

4. We create a child node in the tree for each attribute of the Aij value and the corresponding subset of Cij. Then we label the arc from the current node to the child node with the attribute value Aij. 5