SDAF: Symbolic data classification using variations in frequent terms

2016 ◽  
Author(s):  
Mohamed A. Mahfouz ◽  
Yasser El-Sonbaty ◽  
Mohamed A. Ismail
Keyword(s):  
Data Classification ◽  
Symbolic Data

ANALISIS NILAI KOHESI DAN KOHERENSIDALAM TERJEMAHAN AL-QUR’AN SURAH AL AL ZALZALAH

2016 ◽  
Vol 1 (2) ◽  
pp. 23
Author(s):  
MUNIRAH MUNIRAH ◽  
HUSAIN SYARIFUDDIN
Keyword(s):  
Data Analysis ◽  
Data Collection ◽  
Data Classification ◽  
Research Data ◽  
Relative Pronoun ◽  
Second Person ◽  
Record Keeping ◽  
Descriptive Research ◽  
Qualitative Descriptive

This study aimed to describe the value of cohesion and coherence contained in the translation of the Qur'an surah Al Zalzalah. This study was a qualitative descriptive research, research data collection techniques using three techniques namely, inventory, rading and understanding, and record keeping. The data analysis used the coding of data, classification data, and the determination of the data. The results showed that the cohesion markers used in the translation of surah Al Zalzalah discourse are: 1) reference, 2) pronouns, ie pronouns second person, and third, the relative pronoun, the pronoun pointer, pen pronouns and pronouns owner, 3 ) conjunctions, namely temporal conjunctions, coordinating conjunctions, subordinating conjunctions, and conjunctions koorelatif, and 4) a causal ellipsis. It mean that there was a coherence in the translation of surah Al Zalzalah discourse are: the addition or addition, pronouns, repetition or repetition, match words or synonyms, in whole or in part, a comparison or ratio of conclusions or results. Keywords: Cohesion, Coherence, sura Al Zalzalah AbstrakPenelitian ini bertujuan untuk mendeskripsikan nilai kohesi dan koherensi yang terdapat dalam terjemahan Al-Qur’an surah Al Zalzalah. Jenis penelitian ini termasuk jenis penelitian deskriptif kualitatif, Teknik pengumpulan data penelitian menggunakan tiga teknik yakni, inventarisasi, baca simak, dan pencatatan. Teknik analisis data menggunakan pengodean data, pengklasifikasian data, dan penentuan data. Hasil penelitian menunjukkan bahwa pemarkah kohesi yang digunakan dalam wacana terjemahan surah Al Zalzalah adalah: 1) referensi, 2) pronomina, yaitu kata ganti orang kedua, dan ketiga, kata ganti penghubung, kata ganti penunjuk, kata ganti penanya dan kata ganti empunya, 3) konjungsi, yaitu konjungsi temporal, konjungsi koordinatif, konjungsi subordinatif, dan konjungsi koorelatif, dan 4) elipsis kausal. Sarana koherensi yang terdapat di dalam wacana terjemahan surah Al Zalzalah adalah: penambahan atau adisi, pronomina, pengulangan atau repetisi, padan kata atau sinonim, keseluruhan atau bagian, komparasi atau perbandingan simpulan atau hasil.Kata Kunci: Kohesi, Koherensi, surah Al Zalzalah

Symbolic data race detection for Habanero programs

2019 ◽  
Vol 44 (4) ◽  
pp. 18-18 ◽  
Author(s):  
Egor Namakonov ◽  
Eric Mercer ◽  
Pavel Parizek ◽  
Kyle Storey
Keyword(s):  
Data Race ◽  
Data Race Detection ◽  
Race Detection ◽  
Symbolic Data

scholarly journals Data Reduction in the String Space for Efficient kNN Classification Through Space Partitioning

2020 ◽  
Vol 10 (10) ◽  
pp. 3356 ◽  
Author(s):  
Jose J. Valero-Mas ◽  
Francisco J. Castellanos
Keyword(s):  
Data Reduction ◽  
Structural Data ◽  
Data Representation ◽  
Space Partitioning ◽  
Classification Rate ◽  
Symbolic Data ◽  
Knn Classifier ◽  
Initial Dataset ◽  
Low Efficiency ◽  
High Level

Within the Pattern Recognition field, two representations are generally considered for encoding the data: statistical codifications, which describe elements as feature vectors, and structural representations, which encode elements as high-level symbolic data structures such as strings, trees or graphs. While the vast majority of classifiers are capable of addressing statistical spaces, only some particular methods are suitable for structural representations. The kNN classifier constitutes one of the scarce examples of algorithms capable of tackling both statistical and structural spaces. This method is based on the computation of the dissimilarity between all the samples of the set, which is the main reason for its high versatility, but in turn, for its low efficiency as well. Prototype Generation is one of the possibilities for palliating this issue. These mechanisms generate a reduced version of the initial dataset by performing data transformation and aggregation processes on the initial collection. Nevertheless, these generation processes are quite dependent on the data representation considered, being not generally well defined for structural data. In this work we present the adaptation of the generation-based reduction algorithm Reduction through Homogeneous Clusters to the case of string data. This algorithm performs the reduction by partitioning the space into class-homogeneous clusters for then generating a representative prototype as the median value of each group. Thus, the main issue to tackle is the retrieval of the median element of a set of strings. Our comprehensive experimentation comparatively assesses the performance of this algorithm in both the statistical and the string-based spaces. Results prove the relevance of our approach by showing a competitive compromise between classification rate and data reduction.

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