Set sim,flag=0 //sim is Similarity machine vector //.To illustrate the method the twelve machines are used as an example to be converted in the vectors of features(1 for feature existence, 0 for not) with set of feature to be the dataset for mining task as shown in fig(1). The dataset are then considered to be unique machines, see table (2). The process continues until all vectors are allocated correctly. The calculation of the matching process need efficient function to get the job done accurately and therefore be choose the equality function as in the proposed algorithm ,see in fig(2). The data mining tasks will deal with the first type, while the second type ignored. The first type are essential records and the second type are the similar records,(see in the table 1). Second step is the process of detecting matches between vectors by comparing each one with the other vectors features in the dataset and when there is a similarity between them, the number of the original vector placement to the similar vector is given and so on and hence two types of transactions in the database. The selected database is composed of machines that represent data in an environment or similar other environments to enable the mining algorithms to find the best results, the following steps depict the method used as.ДЇirst, all FSM are converted into vectors of features that are extracted from environment or r environments of similar features to make the database composed of only vectors representing machines. A particular FSM is defined by a set of its states, and the triggering condition for each transition.ДЇsm are important technique of graphs and formulas that define the conceptual graphs and consists of states, transitions, and be in two basic formats. It can transform from one state to another when initiated by a triggering occurrence or condition this is called aВ transition. The machine is in only one state at a time the state it is in at any given time is called theВ current state. It is conceived as anВ abstract machineВ that can be in one of a finite number ofВ states.

finite-state machineВ (FSM) orВ finite-state automatonВ (plural:В automata), or simply aВ state machine, is a arithmeticalВ model of computationВ used to propose bothВ computer programsВ andВ sequential logicВ circuits.

The center of graph mining is to extract important knowledge from graph represented data by techniques from fields such as data mining, machine learning, pattern recognition, statistics and graph theorypattern recognition and graph theory.ДЇinite-state machines offer a simple computational form with many applications. Graph-based data mining has two major approaches: frequent subgraph mining and graph relational data. The graph depiction that set of nodes and links between nodes allow practice of mining algorithm. Graph mining denotes a group of algorithms for mining the relational aspects of data represented as a graph. The graph mining has been raped sudden increase in the data mining.

Using Vectors of Features for Finite State Automata Dataset Reduction. Using Vectors of Features for Finite State Automata Dataset ReductionДЄl-assadi T. KEYWORDS:Дata reduction essential machines FSM graph mining machine matching vectors of featuresДЄl-assadi T. This paper present a method using vectors of features for find machines matching, which is one task of mining graph data, which are frequently found in a single environment or similar environments, thereby reducing the number of records and increase efficiency of mining tasks. A finite state automata is the most important type of graphs ,which is called conceptual graphs, while the expansion of using the graphs in the process of data mining, the use of FSM is still limited because of the difficulty in processing in databases, therefore in order to find methods that make it easier to deal with large groups of machines, as a database, is encourage to use of this type of representation in this paper of graph mining.

Finite state automata adalah