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5 results on '"Sadreddini, M. H."'

1. PTclose: A novel algorithm for generation of closed frequent itemsets form dense and sparse datasets.

Author

Nezhad, J. Tahmores and Sadreddini, M. H.

Subjects
*ALGORITHMS, *DATA mining, *DATABASE searching, *DATABASE marketing, *CONTENT mining
Abstract

In recent years, various algorithms for mining closed frequent itemsets (CFI) have been proposed. Different structures such as prefix sharing are used within these algorithms. However the name challenging problem in many of them is the high requirement of memory, especially in case of sparse datasets. Thus, most of the proposed methods are proper for dense datasets. In this paper we present a new approach to mining closed frequent itemsets using two structures, namely Patricia tree and PTArray. By using these two structures, both the response time and also memory consumption are reduced, significantly. The proposed method, called PTclose is suitable for both dense and sparse datasets. The algorithm is assessed throughout a set of experiments. The results narrate for the relative efficiency of the algorithm compared to other existing methods. [ABSTRACT FROM AUTHOR]

Published
2007

2. IQPI: An Incremental System for Answering Imprecise Queries Using Approximate Dependencies and Concept Similarities.

Author

Ahmad, S. M. Fakhr, Sadreddini, M. H., and Jahromi, M. Zolghadri

Subjects
*QUERY (Information retrieval system), *INFORMATION storage & retrieval systems, *ONLINE databases, *ELECTRONIC directories
Abstract

Most of the proposed systems to process queries over web databases require the user to provide some information regarding the relative importance of attributes and the similarities between nominal values. Recently, a new system called AIMQ has been proposed, which is based on measuring concept similarities. This system is end-user independent and can answer imprecise queries. The main drawback of this system is that it is not incremental. All computations must be repeated when a tuple is added to the database. As a solution to this problem, in this article, we propose an incremental and efficient system called IQPI, which can be considered as the incremental version of AIMQ. In IQPI, the set of approximate dependencies between attributes are mined, first (using our new efficient approach). Using this set of dependencies, the user's imprecise query is converted into some precise queries. Each of the precise queries is then fed into the system and the results are filtered (to obtain most relevant answers) using concept similarity graphs. These graphs are constructed in another part of the system and each edge in a graph represents the similarity between two nominal values. The structure of the similarity graphs are such that the least amount of computation is needed for them to be updated, when database is changed. In dependency mining part of the system, we present a new incremental algorithm that is based on logical operations over bit strings. It is crucial for a search system to be incremental, due to the dynamic nature of the world-wide web. [ABSTRACT FROM AUTHOR]

Published
2007

3. AD-Miner: A new incremental method for discovery of minimal approximate dependencies using logical operations.

Author

Fakhrahmad, S. M., Sadreddini, M. H., and Jahromi, M. Zolghadri

Subjects
*DATA mining, *RELATIONAL databases, *COMPUTATIONAL complexity, *DATABASE searching, *DATA analysis
Abstract

Discovery of possible relations between attribute values in a relational database (i.e., functional dependencies) is an important issue in the field of data mining and knowledge discovery. Many search techniques have been proposed to discover classical and extended functional dependencies; but even the most efficient solutions do not have an acceptable performance in the case of large relation instances. In addition, most of the proposed algorithms assume that the database is static and thus database updates require re-scanning of the entire data repeatedly. In this paper, we propose a new incremental method, AD-Miner, to discover Approximate Dependencies (ADs). The main part of our work is based on logical operations which aim to reduce the computational complexity. The method is incremental and thus avoids re-scans of database when a set of tuples is added to the relation. Our experimental results indicate that our method is more efficient than FastFDs [22] which is one of the most efficient algorithms for mining of perfect dependencies. Furthermore, we have shown that the complexity of our method is lower than major incremental methods namely partitioning and Pair-wise comparison methods. In addition, our method has the extra advantage of marking the index of the tuples that violate a dependency. This feature can be used to find the exceptional cases that are inconsistent with the rest of the data. We have implemented AD-Miner and tested it on several benchmarks and synthetic data. [ABSTRACT FROM AUTHOR]

Published
2008
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4. Machine Translation Based on Data Mining and Deductive Schemes.

Author

Fakhrahmad, S. M., Rezapour, A. R., Sadreddini, M. H., and Jahromi, M. Zolghadri

Subjects
*DATA mining, *MACHINE theory, *NATURAL language processing, *SEQUENTIAL machine theory, *MATHEMATICAL induction, *RECURSIVE functions, *MATHEMATICAL models
Abstract

Machine translation (MT) is one of the most attractive fields in natural language processing. In this paper, we propose some new ideas for designing an MT system. For this purpose, we first introduce a grammatical rule induction method. After representing the extracted knowledge by a set of finite automata, a recursive model is proposed, which uses a combination of rule and example based techniques. In the translation phase, through a hierarchical chunking process, the input sentence is divided into a set of phrases. Each phrase is first searched through the corpus of examples. If the phrase is found, it will not be chunked anymore. Otherwise, the phrase is divided into smaller sub-phrases. The experimental results show a promising accuracy and efficiency of the proposed system. [ABSTRACT FROM AUTHOR]

Published
2012

5. ENTROPY BASED FUZZY RULE WEIGHTING FOR HIERARCHICAL INTRUSION DETECTION.

Author

MOOSAVI, M. R., FAZAELI JAVAN, M., SADREDDINI, M. H., and ZOLGHADRI JAHROMI, M.

Subjects
*COMPUTER networks, *DATA mining, *INTRUSION detection systems (Computer security), *FUZZY logic, *ACCURACY, *INTERNET traffic, *COMPUTER security
Abstract

Predicting different behaviors in computer networks is the subject of many data mining researches. Providing a balanced Intrusion Detection System (IDS) that directly addresses the trade-off between the ability to detect new attack types and providing low false detection rate is a fundamental challenge. Many of the proposed methods perform well in one of the two aspects, and concentrate on a subset of system requirements. There are many non-functional requirements for an applicable and practical IDS. The process should be online, incremental and adaptive to ever changing behaviors of normal users and attackers. Moreover providing comprehensive and interactive IDS could both, enhance the performance of the system and extend the knowledge of domain experts. In this paper, we propose a fuzzy rule-based classification system using a hierarchical rule learning method. In each stage of the hierarchy, a set of rules with certain length of antecedent are investigated. A novel rule weighting method, based on the entropy measure, determines the appropriateness of each rule. The experimental results on KDD99 intrusion detection dataset show the effectiveness of the proposed method in tackling the tradeoff between accuracy and comprehensibility of fuzzy rule-based systems. Although the dimension of antecedents is not limited, the resultant rule-base contains a small number of complex rules, which are essential to reach the desired accuracy. [ABSTRACT FROM AUTHOR]

Published
2014

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