This is an essential condition for a signal to be the trajectory of a moving particle. It has been shown that a signal s(t) is traceable on a piece of paper or in an oscilloscope, only if s'(t) exists on all but at most a finite number of points within any finite interval. A new entropy measure called semantic entropy has been introduced.

It has been shown that in any analog signal semantic information can be encoded at a point in the form of the shape of its infinitesimal neighborhood in 17 distinct ways. The DFA has been generalized to a weighted finite state transducer (WFST), which has been used to identify action potentials in a spike train and also to distinguish two speakers when uttering the same phoneme. A deterministic finite automaton (DFA) has been designed which can accept any finite length digital signal and therefore collection of all finite length digital signals forms a regular language. Considering the sign changes of P(s) it has been shown that in the smallest neighborhood of n, in which n is the middle point, semantic information in s can be encoded in 13 distinct ways. After s(t) is digitized (to make it s) the discrete form P(s) is valid. Assuming meaning of the signal or the semantic information is in its shape, we can say that P(s(t)) is the rate at which kinetic energy of the particle is dissipated to encode semantic information in s(t) at t. Then the power of the particle at point t is P(s(t)) = s'(t)s'(t), which is the rate at which kinetic energy is dissipated (assuming the mass of the particle is unit) by the particle in order to create the trajectory or give shape to the signal. The inclusive approach is also used to incorporate the number of constraints in the data mining process to generate user-centric patterns.Ī one dimensional time domain analog signal s(t) can be visualized as a trajectory of a moving particle in a force field with one degree of freedom. This algorithm improves the accuracy rate and develops robust methods for sequential pattern mining. The use of a Turing machine can claim a higher accuracy rate in recognizing sequential patterns. JFLAP has been a widely used and most successful tool for visualizing and simulating all types of automata. The JFLAP (Java Formal Languages and Automata Package) tool is used to construct a Turing machine as well as reconstruction of it. So, the use of the Turing machine will be applied constraints for recognition of pattern and therefore, it is possible to discover more user-centered patterns. The use of Turing machine for sequential pattern mining is a flexible specification tool because the Turing machine accepts all types of grammars and hence, there is no need to recognize the category of grammar for constraints. The Turing machine is a core part of Artificial Intelligence (AI). The researcher has proposed a new algorithm entitled Sequential Pattern Mining using Turing Machine (SPMTM) for sequential pattern mining.

Also, it generates an effective and efficient algorithm that is generic for all domains. To generate optimum frequent sequences as per user expectation, minimize and explore the memory size by specifying the constraint type, and to generate only frequent sequences those are satisfied by given constraint. The principal objective of this research is to find out the complete set of patterns without repetition of scanning the database. To overcome these problems, there are various challenges such as to find the complete set of patterns for a minimum threshold, to incorporate the various kinds of user-specific constraints, to study target-oriented sequential pattern mining and the application in some real dataset. It is difficult to mining long sequential patterns, hence the use of frequency as the interesting measures an exponential number of sequential patterns and an optimum performance of the algorithms is always desired for the feasible operation of applications. The various research problems arising in sequential pattern mining require multiple scans of the original databases in the mining process, so the maximum cost is required. In recent years, sequential pattern mining is widely used to a number of application domains such as E-commerce, Medical Science, Information Technology, Telecommunication, Education, etc.

The sequential pattern mining is a technique useful for retrieval of data, hidden patterns and information processing from the databases. Today the retrieval of data, hidden patterns, and information processing are critical tasks. The storage capacity of data is increasing massively. In the present scenario, almost all the work becoming computerized, so all the data and information is processed and stored in the computer system.

Max 4 live otomata