Search

Clustering represents one of the most popular and used Data Mining techniques due to its usefulness and the wide variations of the applications in real world. Defining the number of the clusters required is an application oriented context, this means that the number of clusters k is an input to the whole clustering process. The proposed approach represents a solution for estimating the optimum number of clusters. It is based on the use of iterative K-means clustering under three different criteria; centroids convergence, total distance between the objects and the cluster centroid and the number of migrated objects which can be used effectively to ensure better clustering accuracy and performance. A total of 20000 records available on the internet were used in the proposed approach to test the approach. The results obtained from the approach showed good improvement on clustering accuracy and algorithm performance over the other techniques where centroids convergence represents a major clustering criteria. C# and Microsoft Excel were the software used in the approach.

Clustering represents one of the most popular knowledge extraction algorithms in data mining techniques. Hierarchical and partitioning approaches are widely used in this field. Each has its own advantages, drawbacks and goals. K-means represents the most popular partitioning clusteringtechnique, however it suffers from two major drawbacks; time complexity and its sensitivity to the initial centroid values. The work in this paper presents an approach for estimating the starting initial centroids throughout three process including density based, normalization and smoothing ideas. The proposed algorithm has a strong mathematical foundation. The proposed approach was tested using a free standard data (20000 records). The results showed that the approach has better complexity and ensures the clustering convergence.

The work in this paper presents a proposed solution for preprocessing, analyzing, mining and data warehouse model for personal medical data collected from different hospitals and clinics. The proposed solution contains different phases and steps, including Extraction, Transforming and Loading (ETL) and data preprocessing focuses on converting the logged data into categories suitable for analysis and mining process, a star warehouse model was implemented that fulfills the required processing techniques, data are represented by multi-dimensional cubes for efficient and better data representation, and finally link analysis was applied on the data. The proposed framework is simple and straight forward for implementation. Personal medical data from different sources mostly in Excel files were converted into clean, complete and consistent data by different preprocessing techniques. Logged data were converted into high quality, reliable and suitable for analysis and mining process. Star warehouse schema was implemented since it is very suitable for such type of data and mining techniques. 19900 patients records were collected and used in this work. Excel and WEKA software were used for the analysis and mining processes.

Search algorithms play an essential role in wide variety of applications in computer science. Different search algorithms can be applied to different data structures. Each data structure has its own advantages and limitations which in turn affect the search algorithm complexity and performance. In the context of Big Data such limitations can be more obvious and noticeable in reducing the performance offered by the data structure and search algorithms applied to it because of big data characteristics such as its huge volume. Also, string data sets can be more challenging. They can cause more overhead in preprocessing. Some string datasets such as personal names may have some skew that has negative effect on hash indexes by increasing the ratio of collisions produced. The work in this paper offers a technique for improving the famous Hash table Data Structure by reducing the collision ratio in hashing in the average case, thus reducing the total number of comparisons made by search algorithm. The advantage of reducing the collision ratio was achieved by building a hybrid approach making use of three common data structures, namely Hash Table, B-Tree and Linked List. The results obtained showed noticeable improvement for time complexity of data searching algorithm over the traditional data structures that were used for comparisons.

Solving transportation problems where products to be supplied from one side(sources) to another (demands) with a goal to minimize the overall transportation cost represents an activity of great importance. Most of the works done in the field deals with the problem as two-sided model (Sources such as factories and Demands such as warehouses) with no connections between sources or demands. However, real world transportation problems may come in another model where sources are connected in a network like graph in which each source may supply other sources in a specific cost. The work in this paper suggests an algorithm and a graph model with mathematical solution for finding the minimum feasible solution for such widely used transportation problems. In this work, the graph representing the problem in which all sources are connected together in a network model with specific cost on each edge is converted into a new graph where additional virtual sources representing supplies between sources are added to the graph , new costs between the added sources and the demands are also calculated, and then modified Kruskal’s algorithm is applied to get the minimum feasible solution. The proposed solution is a straight forward model with strong mathematical and graph models. It can be widely used for solving real world transportation problems with feasible time and space complexity where time complexity of O(E2 + V2) is required, where E represents the number of edges and V represents the number of vertices. Different numerical examples were used to study the effectiveness and correctness of the proposed algorithm.

Text classification (TC) is an essential field in both text mining (TM) and natural language processing (NLP). Humans have a tendency to organize and categorize everything as they want to make things easier to understand. Therefore, text classification is an important step to achieve this goal. Arabic text classification (ATC) is a difficult process because the Arabic language has complications and limitations resulting from the nature of its morphology. In this paper, a proposed approach called the Master-Slaves technique (MST) was used to improve Arabic text classification. It consists of two main phases: in the first phase, a new Arabic corpus of 16757 text files was collected. These text files were classified into five categories manually. In the second phase, four different classifiers were implemented on the collected corpus. These classifiers are Naive Bayes (NB), K-Nearest Neighbour (KNN), Multinomial Logistic Regression (MLR) and Maximum Weight (MW). Naive Bayes classifier was implemented as Master and the others as Slaves. The results of these slave classifiers were used to change the probability of the Naive Bayes classifier (Master). The four classifiers used were implemented individually and the simple voting technique was implemented among them too on the collected corpus to check the effectiveness and efficiency of the proposed technique. All the tests were applied after the pre-processing of Arabic text documents (tokenization, stemming, and stop-word removal) and each document was represented as vector of weights. For the reliability of the results, 10-fold cross-validation was used in this paper. The results showed that the Master-slaves technique gives a good improvement in accuracy of text document classification with accepted algorithm complexity compared to other techniques.

In this study we used amplified fragment length polymorphism (AFLP) and microsatellite (short sequence repeat or SSR) markers to identify a major quantitative trail locus (QTL) for yellow spot (Mycovellosiella koepkei) disease resistance in sugarcane. A bi-parental cross between a resistant variety, M 134/75, and a susceptible parent, R 570, generated a segregating population of 227 individuals. These clones were evaluated for yellow spot infection in replicated field trials in two locations across two consecutive years. A χ2-test (χ2 at 98% confidence level) of the observed segregation pattern for yellow spot infection indicated a putative monogenic dominant inheritance for the trait with a 3 (resistant):1(susceptible) ratio. The AFLP and SSR markers identified 666 polymorphisms as being present in the resistant parent and absent in the susceptible one. A genetic map of M 134/75 was constructed using 557 single-dose polymorphisms, resulting in 95 linkage groups containing at least two markers based on linkages in coupling. QTL analysis using QTLCartographer v1.17d and MAPMAKER/QTL v1.1 identified a single major QTL located on LG87, flanked by an AFLP marker, actctc10, and an SSR marker, CIR12284. This major QTL, which was found to be linked at 14 cM to an AFLP marker, was detected with LOD 8.7, had an additive effect of −10.05% and explained 23.8% of the phenotypic variation of yellow spot resistance.

Yellow leaf syndrome (YLS) is a recently reported disease of sugarcane, characterized by yellowing of the leaves. Two pathogens: a virus, Sugarcane yellow leaf virus (SCYLV); and a phytoplasma, sugarcane yellows phytoplasma (SCYP), are associated with the disease. The use of tissue culture was investigated as a means to eliminate both SCYLV and SCYP from exotic varieties undergoing quarantine in Mauritius. Of 43 varieties in quarantine, 28 were infected with SCYLV and 19 with SCYP when checked by RT–PCR and nested PCR, respectively. Seventeen varieties were coinfected with both pathogens. Thirty infected varieties were induced to form callus in vitro using leaf rolls as explants. After two subcultures, 19 varieties were successfully regenerated and tested for SCYLV and SCYP. No pathogen could be detected in any regenerated plantlets. All the regenerated plants remained free from both SCYLV and SCYP over a period of 1 year in the glasshouse, confirming that the pathogens had been eliminated by tissue culture.

Using the polymerase chain reaction (PCR), reverse-transcriptase–PCR (RT–PCR) and double-antibody-sandwich enzyme-linked immunosorbent assay (DAS–ELISA), a phytoplasma (sugarcane yellows phytoplasma, ScYP) and a virus (Sugarcane yellow leaf virus, ScYLV) were detected in sugarcane with yellow leaf syndrome (YLS) in Mauritius. Samples were collected from clones undergoing quarantine, in a variety-collection plot and in commercial fields. A 1·25 kb DNA fragment encoding the phytoplasma 16S rRNA was consistently amplified by nested PCR. Of 134 samples with and without symptoms derived from 113 varieties, 111 were infected by either ScYLV or ScYP. The phytoplasma was detected in 63 samples by PCR. Restriction fragment-length polymorphism (RFLP) analysis of the phytoplasma 16S rDNA amplified product indicated that sugarcane yellows phytoplasma group III, which is related to Western X phytoplasma, is present in Mauritius. ScYLV was detected by RT–PCR and ELISA. The virus was more widely distributed than the phytoplasma, and was found in 70 and 100 samples by ELISA and RT–PCR, respectively. There was a significant correlation between the presence of the phytoplasma and YLS symptoms, while such correlation was not significant for ScYLV detected by RT–PCR. ELISA was less sensitive than RT–PCR for detection of ScYLV. Forty-one samples were coinfected with both microorganisms. Eighty-five per cent of the samples displayed symptoms when ScYLV and SCYP coexisted, while 55 and 38% were observed when ScYP or ScYLV, respectively, was present alone. The results indicate that the presence of both organisms enhanced the syndrome.

Some factors which affect the in vitro propagation of wild pear ( Pyrus syrica ) were studied. Successful shoot proliferation was obtained on Murashige and Skoog medium supplemented with 1.5–2.0 mg 1 −1 benzyl adenine. Shoot height decreased to 1.8–1.6 cm and 1.3 cm with benzyl adenine (1.5–2.0 mg 1 −1 ) and zeatin (1.0–2.0 mg 1 −1 ), respectively. Benzyl adenine (at 2.0 mg 1 −1 ) and zeatin (at 1.5–2.0 mg 1 −1 ) enhanced callus formation. Indole butyric acid, indole acetic acid and naphthalene acetic acid induced in vitro rooting and a maximum of 72% rooting was achieved at 3.0 mg 1 −1 indole acetic acid. Neither activated charcoal nor polyvinylpyrrolidone influenced in vitro rooting. Ex vitro rooting was unsuccessful when treating shoot microcuttings with 0.0, 5.0, 10.0, or 15.0 mg 1 −1 indole butyric acid, indole acetic acid, or naphthalene acetic acid for l h and grown under intermittent water mist. An average of 95% survival was achieved when rooted microshoots were acclimatized under glass or plastic covers using intermittent mist in the greenhouse.

Much of our most basic understanding of genetics has its roots in plant genetics and crop breeding. The study of plants has led to important insights into highly conserved biological process and a wealth of knowledge about development. Agriculture is now well positioned to take its share benefit from genomics. The primary sequences of most plant genes will be determined over the next few years. Informatics and functional genomics will help identify those genes that can be best utilized to crop production and quality through genetic engineering and plant breeding. Recent developments in plant genomics are reviewed.

A pot experiment was conducted to study the interaction between soil salinity and two major nutrients, nitrogen and phosphorus, and the effect of this interaction on the growth and ionic composition of corn plants.

In recent years, tomato plants (Lycopersicon esculentum Mill., cultivar MST 32/1) grown in Mauritius were observed with symptoms characteristic of a phytoplasmal infection. Young plants exhibited stunted growth, with a bunchy top symptom on one or more stems. The leaves were small with a curled margin, sometimes were purplish, and were clustered as a rosette. On older plants, other leaf symptoms, such as necrotic vein, mosaic, and vein clearing, that were typical of viral infection were observed. In March to June 1998, research was initiated to determine whether a phytoplasma or a virus was associated with the problem. Virus screening was carried out by double antibody sandwich enzyme-linked immuno-sorbent assay (DAS-ELISA) with the following antisera: tomato mosaic virus (TMV), tomato leaf curl virus (TLCV), tomato spotted wilt virus (TSWV), cucumber mosaic virus (CMV), potato virus X (PVX), potato virus Y (PVY), and potato leaf roll virus (PLRV). The presence of a phytoplasma was assayed by the polymerase chain reaction (PCR) with universal primers that amplify the 16S rDNA sequences. Oligonucleotide pairs RU3 and FU5 (1) and P1 and P6 (2) were evaluated separately. Template DNA was prepared from young leaves with the Nucleon Phyta-Pure Plant DNA extraction kit (catalog no. RPN 8511, Amersham Life Science, Buckinghamshire, U.K.). Amplification of a 880-bp or a 1,400-bp product with the two primer pairs confirmed the presence of a phyto-plasma in the tomato plants with the bunchy top symptoms. No amplification was obtained from plants that had both bunchy top and necrotic symptoms on the leaves and that were found to be infected with PVY by ELISA. To determine whether phenolics and other inhibitors from the virus-induced necrotic tissue interfered with the detection of phytoplasmas, plant DNA from these tissues was spiked with known phytoplasmal DNA. Amplification was not successful, confirming the presence of PCR inhibitors. Further purification of the DNA with phenol/chloroform before precipitation helped to obtain DNA of a purer quality from which a positive band representing the sequence of a phytoplasma was amplified. This is the first report of a phytoplasma in tomato in Mauritius. References: (1) U. Ahrens and E. Seemüller. Phytopathology 82:828, 1992. (2) S. Deng and D. Hiruku. J. Microbiol. Methods 14:53, 1991.